28 CFR 54.310 - Recruitment.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Recruitment. 54.310 Section 54.310... in Admission and Recruitment Prohibited § 54.310 Recruitment. (a) Nondiscriminatory recruitment. A... recruitment and admission of students. A recipient may be required to undertake additional recruitment efforts...

40 CFR 5.310 - Recruitment.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Recruitment. 5.310 Section 5.310... in Admission and Recruitment Prohibited § 5.310 Recruitment. (a) Nondiscriminatory recruitment. A... recruitment and admission of students. A recipient may be required to undertake additional recruitment efforts...

43 CFR 41.310 - Recruitment.

Science.gov (United States)

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Recruitment. 41.310 Section 41.310 Public... in Admission and Recruitment Prohibited § 41.310 Recruitment. (a) Nondiscriminatory recruitment. A... recruitment and admission of students. A recipient may be required to undertake additional recruitment efforts...

14 CFR 1253.310 - Recruitment.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Recruitment. 1253.310 Section 1253.310... in Admission and Recruitment Prohibited § 1253.310 Recruitment. (a) Nondiscriminatory recruitment. A... recruitment and admission of students. A recipient may be required to undertake additional recruitment efforts...

6 CFR 17.310 - Recruitment.

Science.gov (United States)

2010-01-01

... 6 Domestic Security 1 2010-01-01 2010-01-01 false Recruitment. 17.310 Section 17.310 Domestic... in Admission and Recruitment Prohibited § 17.310 Recruitment. (a) Nondiscriminatory recruitment. A... recruitment and admission of students. A recipient may be required to undertake additional recruitment efforts...

48 CFR 310.001 - Policy.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Policy. 310.001 Section 310.001 Federal Acquisition Regulations System HEALTH AND HUMAN SERVICES COMPETITION AND ACQUISITION PLANNING MARKET RESEARCH § 310.001 Policy. (a) OPDIVs are encouraged to conduct market research, to the...

13 CFR 113.310 - Recruitment.

Science.gov (United States)

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false Recruitment. 113.310 Section 113... Discrimination on the Basis of Sex in Admission and Recruitment Prohibited § 113.310 Recruitment. (a) Nondiscriminatory recruitment. A recipient to which §§ 113.300 through 113.310 apply shall not discriminate on the...

36 CFR 1211.310 - Recruitment.

Science.gov (United States)

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Recruitment. 1211.310 Section... Discrimination on the Basis of Sex in Admission and Recruitment Prohibited § 1211.310 Recruitment. (a) Nondiscriminatory recruitment. A recipient to which §§ 1211.300 through 1211.310 apply shall not discriminate on the...

18 CFR 1317.310 - Recruitment.

Science.gov (United States)

2010-04-01

... 18 Conservation of Power and Water Resources 2 2010-04-01 2010-04-01 false Recruitment. 1317.310... Discrimination on the Basis of Sex in Admission and Recruitment Prohibited § 1317.310 Recruitment. (a) Nondiscriminatory recruitment. A recipient to which §§ 1317.300 through 1317.310 apply shall not discriminate on the...

38 CFR 23.310 - Recruitment.

Science.gov (United States)

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Recruitment. 23.310... Discrimination on the Basis of Sex in Admission and Recruitment Prohibited § 23.310 Recruitment. (a) Nondiscriminatory recruitment. A recipient to which §§ 23.300 through 23.310 apply shall not discriminate on the...

Introduction to Time Series Modeling

CERN Document Server

Kitagawa, Genshiro

2010-01-01

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f

7 CFR 1250.310 - Promotion.

Science.gov (United States)

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false Promotion. 1250.310 Section 1250.310 Agriculture... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE EGG RESEARCH AND PROMOTION Egg Research and Promotion Order Definitions § 1250.310 Promotion. Promotion means any action, including paid...

20 CFR 628.310 - Administration.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Administration. 628.310 Section 628.310 Employees' Benefits EMPLOYMENT AND TRAINING ADMINISTRATION, DEPARTMENT OF LABOR PROGRAMS UNDER TITLE II OF THE JOB TRAINING PARTNERSHIP ACT State Programs § 628.310 Administration. Funds provided to the...

7 CFR 3052.310 - Financial statements.

Science.gov (United States)

2010-01-01

... 7 Agriculture 15 2010-01-01 2010-01-01 false Financial statements. 3052.310 Section 3052.310....310 Financial statements. (a) Financial statements. The auditee shall prepare financial statements... appropriate, cash flows for the fiscal year audited. The financial statements shall be for the same...

13 CFR 134.310 - Discovery.

Science.gov (United States)

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false Discovery. 134.310 Section 134.310 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION RULES OF PROCEDURE GOVERNING CASES BEFORE THE... Designations § 134.310 Discovery. Discovery will not be permitted in appeals from size determinations or NAICS...

43 CFR 3.10 - Reports.

Science.gov (United States)

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Reports. 3.10 Section 3.10 Public Lands: Interior Office of the Secretary of the Interior PRESERVATION OF AMERICAN ANTIQUITIES § 3.10 Reports. At the close of each season's field work the permitee shall report in duplicate to the Smithsonian...

7 CFR 1980.310 - Loan purposes.

Science.gov (United States)

2010-01-01

... 7 Agriculture 14 2010-01-01 2009-01-01 true Loan purposes. 1980.310 Section 1980.310 Agriculture... REGULATIONS (CONTINUED) GENERAL Rural Housing Loans § 1980.310 Loan purposes. The purpose of a loan guaranteed... applicant as a primary residence. The loan may be to purchase a new dwelling or an existing dwelling. The...

32 CFR 310.46 - Civil actions.

Science.gov (United States)

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false Civil actions. 310.46 Section 310.46 National Defense Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Privacy Act Violations § 310.46 Civil actions. An individual may file a civil suit...

32 CFR 310.20 - Reproduction fees.

Science.gov (United States)

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false Reproduction fees. 310.20 Section 310.20... PROGRAM DOD PRIVACY PROGRAM Access by Individuals § 310.20 Reproduction fees. (a) Assessing fees. (1) Charge the individual only the direct cost of reproduction. (2) Do not charge reproduction fees if...

46 CFR 153.310 - Ventilation system type.

Science.gov (United States)

2010-10-01

... 46 Shipping 5 2010-10-01 2010-10-01 false Ventilation system type. 153.310 Section 153.310... Handling Space Ventilation § 153.310 Ventilation system type. A cargo handling space must have a permanent forced ventilation system of the exhaust type. ...

32 CFR 310.47 - Civil remedies.

Science.gov (United States)

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false Civil remedies. 310.47 Section 310.47 National Defense Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Privacy Act Violations § 310.47 Civil remedies. In addition to specific remedial...

31 CFR 542.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 542.310 Section 542.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....310 United States. The term United States means the United States, its territories and possessions...

31 CFR 548.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 548.310 Section 548.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....310 United States. The term United States means the United States, its territories and possessions...

31 CFR 546.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 546.310 Section 546.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF....310 United States. The term United States means the United States, its territories and possessions...

31 CFR 547.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 547.310 Section 547.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... General Definitions § 547.310 United States. The term United States means the United States, its...

Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...

44 CFR 19.310 - Recruitment.

Science.gov (United States)

2010-10-01

... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Recruitment. 19.310 Section... RECEIVING FEDERAL FINANCIAL ASSISTANCE Discrimination on the Basis of Sex in Admission and Recruitment Prohibited § 19.310 Recruitment. (a) Nondiscriminatory recruitment. A recipient to which §§ 19.300 through 19...

46 CFR 310.11 - Cadet uniforms.

Science.gov (United States)

2010-10-01

... for State, Territorial or Regional Maritime Academies and Colleges § 310.11 Cadet uniforms. Cadet uniforms shall be supplied at the school in accordance with the uniform regulations of the School. Those... 46 Shipping 8 2010-10-01 2010-10-01 false Cadet uniforms. 310.11 Section 310.11 Shipping MARITIME...

31 CFR 543.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 543.310 Section 543.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 543.310 United States. The term United States means the United States, its territories and...

31 CFR 588.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 588.310 Section 588.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... Definitions § 588.310 United States. The term United States means the United States, its territories and...

31 CFR 544.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 544.310 Section 544.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... REGULATIONS General Definitions § 544.310 United States. The term United States means the United States, its...

31 CFR 541.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 541.310 Section 541.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF... § 541.310 United States. The term United States means the United States, its territories and possessions...

16 CFR 310.2 - Definitions.

Science.gov (United States)

2010-01-01

... transaction pursuant to which the account will be charged. (x) Prize means anything offered, or purportedly... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Definitions. 310.2 Section 310.2 Commercial... person to access a customer's or donor's account, such as a credit card, checking, savings, share or...

32 CFR 310.36 - OMB training guidelines.

Science.gov (United States)

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false OMB training guidelines. 310.36 Section 310.36... PROGRAM DOD PRIVACY PROGRAM Training Requirements § 310.36 OMB training guidelines. The OMB guidelines (OMB Privacy Guidelines, 40 FR 28948 (July 9, 1975) require all agencies additionally to: (a) Instruct...

46 CFR 310.3 - Schools and courses.

Science.gov (United States)

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Schools and courses. 310.3 Section 310.3 Shipping... Minimum Standards for State, Territorial or Regional Maritime Academies and Colleges § 310.3 Schools and courses. (a) Schools with Federal aid. The following schools are presently operating with Federal aid...

12 CFR 1102.310 - Service of process.

Science.gov (United States)

2010-01-01

... 12 Banks and Banking 7 2010-01-01 2010-01-01 false Service of process. 1102.310 Section 1102.310... Office, Procedures, Public Information § 1102.310 Service of process. (a) Service. Any subpoena or other... 20006. Where the ASC is named as a party, service of process shall be made pursuant to the Federal Rules...

32 CFR 310.42 - Reports control symbol.

Science.gov (United States)

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false Reports control symbol. 310.42 Section 310.42... PROGRAM DOD PRIVACY PROGRAM Reports § 310.42 Reports control symbol. Any report established by this subpart in support of the Privacy Program shall be assigned Report Control Symbol DD-COMP(A)1379. ...

46 CFR 177.310 - Satisfactory service as a design basis.

Science.gov (United States)

2010-10-01

... 46 Shipping 7 2010-10-01 2010-10-01 false Satisfactory service as a design basis. 177.310 Section... (UNDER 100 GROSS TONS) CONSTRUCTION AND ARRANGEMENT Hull Structure § 177.310 Satisfactory service as a design basis. When scantlings for the hull, deckhouse, and frames of the vessel differ from those...

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

Science.gov (United States)

Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

2015-02-01

We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

10 CFR 1021.310 - Environmental impact statements.

Science.gov (United States)

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Environmental impact statements. 1021.310 Section 1021.310 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NATIONAL ENVIRONMENTAL POLICY ACT IMPLEMENTING PROCEDURES Implementing Procedures § 1021.310 Environmental impact statements. DOE shall prepare and...

15 CFR 310.2 - Definitions.

Science.gov (United States)

2010-01-01

... Assistant Secretary for Export Development, International Trade Administration, Department of Commerce. (e... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Definitions. 310.2 Section 310.2 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL...

9 CFR 381.310 - Personnel and training.

Science.gov (United States)

2010-01-01

... INSPECTION AND CERTIFICATION POULTRY PRODUCTS INSPECTION REGULATIONS Canning and Canned Products § 381.310... 9 Animals and Animal Products 2 2010-01-01 2010-01-01 false Personnel and training. 381.310 Section 381.310 Animals and Animal Products FOOD SAFETY AND INSPECTION SERVICE, DEPARTMENT OF AGRICULTURE...

41 CFR 101-4.310 - Recruitment.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 2 2010-07-01 2010-07-01 true Recruitment. 101-4.310... Admission and Recruitment Prohibited § 101-4.310 Recruitment. (a) Nondiscriminatory recruitment. A recipient... recruitment and admission of students. A recipient may be required to undertake additional recruitment efforts...

Multiple Indicator Stationary Time Series Models.

Science.gov (United States)

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

31 CFR 515.310 - Transfer.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Transfer. 515.310 Section 515.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN..., or other fiduciary; the creation or transfer of any lien; the issuance, docketing, filing, or the...

31 CFR 500.310 - Transfer.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Transfer. 500.310 Section 500.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN..., or other fiduciary; the creation or transfer of any lien; the issuance, docketing, filing, or the...

13 CFR 130.310 - Area of service.

Science.gov (United States)

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false Area of service. 130.310 Section 130.310 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION SMALL BUSINESS DEVELOPMENT CENTERS § 130.310 Area of service. The AA/SBDC shall designate in writing the Area of Service of each...

40 CFR 1065.310 - Torque calibration.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 32 2010-07-01 2010-07-01 false Torque calibration. 1065.310 Section... Conditions § 1065.310 Torque calibration. (a) Scope and frequency. Calibrate all torque-measurement systems including dynamometer torque measurement transducers and systems upon initial installation and after major...

46 CFR 310.63 - Uniforms and textbooks.

Science.gov (United States)

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Uniforms and textbooks. 310.63 Section 310.63 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION TRAINING MERCHANT MARINE TRAINING Admission and Training of Midshipmen at the United States Merchant Marine Academy § 310.63 Uniforms and textbooks. The Academy shall supply midshipmen uniforms an...

31 CFR 593.310 - Transfer.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Transfer. 593.310 Section 593.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN..., trustee, or fiduciary; the creation or transfer of any lien; the issuance, docketing, filing, or levy of...

31 CFR 535.310 - Transfer.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Transfer. 535.310 Section 535.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN..., or fiduciary; the creation or transfer of any lien; the issuance, docketing, filing, or the levy of...

42 CFR 457.310 - Targeted low-income child.

Science.gov (United States)

2010-10-01

... family income at or below 200 percent of the Federal poverty line for a family of the size involved; (ii... 42 Public Health 4 2010-10-01 2010-10-01 false Targeted low-income child. 457.310 Section 457.310... Requirements: Eligibility, Screening, Applications, and Enrollment § 457.310 Targeted low-income child. (a...

21 CFR 333.310 - Acne active ingredients.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 5 2010-04-01 2010-04-01 false Acne active ingredients. 333.310 Section 333.310... FOR HUMAN USE TOPICAL ANTIMICROBIAL DRUG PRODUCTS FOR OVER-THE-COUNTER HUMAN USE Topical Acne Drug Products § 333.310 Acne active ingredients. The active ingredient of the product consists of any of the...

32 CFR 310.25 - Disclosure accounting.

Science.gov (United States)

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false Disclosure accounting. 310.25 Section 310.25....25 Disclosure accounting. (a) Disclosure accountings. (1) Keep an accurate record of all disclosures... accounting is required even if the individual has consented to the disclosure of the information. (3...

A neuro-fuzzy computing technique for modeling hydrological time series

Science.gov (United States)

Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

2004-05-01

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.

46 CFR 28.310 - Launching of survival craft.

Science.gov (United States)

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Launching of survival craft. 28.310 Section 28.310... Operate With More Than 16 Individuals on Board § 28.310 Launching of survival craft. A gate or other... each survival craft which weighs more than 110 pounds (489 Newtons), to allow the survival craft to be...

A four-stage hybrid model for hydrological time series forecasting.

Science.gov (United States)

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

Science.gov (United States)

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

Science.gov (United States)

Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

2000-09-01

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

forecasting with nonlinear time series model: a monte-carlo

African Journals Online (AJOL)

PUBLICATIONS1

erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.

29 CFR 99.310 - Financial statements.

Science.gov (United States)

2010-07-01

... 29 Labor 1 2010-07-01 2010-07-01 true Financial statements. 99.310 Section 99.310 Labor Office of the Secretary of Labor AUDITS OF STATES, LOCAL GOVERNMENTS, AND NON-PROFIT ORGANIZATIONS Auditees § 99... have separate audits in accordance with § 99.500(a) and prepare separate financial statements. (b...

Time series modeling, computation, and inference

CERN Document Server

Prado, Raquel

2010-01-01

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

24 CFR 3285.310 - Pier location and spacing.

Science.gov (United States)

2010-04-01

... URBAN DEVELOPMENT MODEL MANUFACTURED HOME INSTALLATION STANDARDS Foundations § 3285.310 Pier location... vertical or horizontal design loads. 5. When a full-height mating wall does not support the ridge beam... wall that are less than 48 inches in width. Place piers on both sides of mating wall openings that are...

42 CFR 495.310 - Medicaid provider incentive payments.

Science.gov (United States)

2010-10-01

... 42 Public Health 5 2010-10-01 2010-10-01 false Medicaid provider incentive payments. 495.310 Section 495.310 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN... INCENTIVE PROGRAM Requirements Specific to the Medicaid Program § 495.310 Medicaid provider incentive...

Models for dependent time series

CERN Document Server

Tunnicliffe Wilson, Granville; Haywood, John

2015-01-01

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater

49 CFR 1016.310 - Judicial review.

Science.gov (United States)

2010-10-01

... 49 Transportation 8 2010-10-01 2010-10-01 false Judicial review. 1016.310 Section 1016.310 Transportation Other Regulations Relating to Transportation (Continued) SURFACE TRANSPORTATION BOARD, DEPARTMENT... Judicial review. Judicial review of final Board decisions on awards may be sought as provided in 5 U.S.C...

31 CFR 587.310 - United States.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false United States. 587.310 Section 587...) MILOSEVIC SANCTIONS REGULATIONS General Definitions § 587.310 United States. The term United States means the United States, its territories and possessions, and all areas under the jurisdiction or authority...

45 CFR 310.0 - What does this part cover?

Science.gov (United States)

2010-10-01

... COMPUTERIZED TRIBAL IV-D SYSTEMS AND OFFICE AUTOMATION General Provisions § 310.0 What does this part cover... 45 Public Welfare 2 2010-10-01 2010-10-01 false What does this part cover? 310.0 Section 310.0... and Office Automation including: (a) The automated systems options for comprehensive Tribal IV-D...

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual

Self-organising mixture autoregressive model for non-stationary time series modelling.

Science.gov (United States)

Ni, He; Yin, Hujun

2008-12-01

Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

31 CFR 545.310 - The Taliban.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false The Taliban. 545.310 Section 545.310 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) OFFICE OF FOREIGN... also known as the “Taleban,” “Islamic Movement of Taliban,” “the Taliban Islamic Movement,” “Talibano...

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

Science.gov (United States)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

A 310-bp minimal promoter mediates smooth muscle cell-specific expression of telokin.

Science.gov (United States)

Smith, A F; Bigsby, R M; Word, R A; Herring, B P

1998-05-01

A cell-specific promoter located in an intron of the smooth muscle myosin light chain kinase gene directs transcription of telokin exclusively in smooth muscle cells. Transgenic mice were generated in which a 310-bp rabbit telokin promoter fragment, extending from -163 to +147, was used to drive expression of simian virus 40 large T antigen. Smooth muscle-specific expression of the T-antigen transgene paralleled that of the endogenous telokin gene in all smooth muscle tissues except uterus. The 310-bp promoter fragment resulted in very low levels of transgene expression in uterus; in contrast, a transgene driven by a 2.4-kb fragment (-2250 to +147) resulted in high levels of transgene expression in uterine smooth muscle. Telokin expression levels correlate with the estrogen status of human myometrial tissues, suggesting that deletion of an estrogen response element (ERE) may account for the low levels of transgene expression driven by the 310-bp rabbit telokin promoter in uterine smooth muscle. Experiments in A10 smooth muscle cells directly showed that reporter gene expression driven by the 2.4-kb, but not 310-bp, promoter fragment could be stimulated two- to threefold by estrogen. This stimulation was mediated through an ERE located between -1447 and -1474. Addition of the ERE to the 310-bp fragment restored estrogen responsiveness in A10 cells. These data demonstrate that in addition to a minimal 310-bp proximal promoter at least one distal cis-acting regulatory element is required for telokin expression in uterine smooth muscle. The distal element may include an ERE between -1447 and -1474.

Modeling of Volatility with Non-linear Time Series Model

OpenAIRE

Kim Song Yon; Kim Mun Chol

2013-01-01

In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

Time series modeling in traffic safety research.

Science.gov (United States)

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

9 CFR 318.310 - Personnel and training.

Science.gov (United States)

2010-01-01

... Section 318.310 Animals and Animal Products FOOD SAFETY AND INSPECTION SERVICE, DEPARTMENT OF AGRICULTURE AGENCY ORGANIZATION AND TERMINOLOGY; MANDATORY MEAT AND POULTRY PRODUCTS INSPECTION AND VOLUNTARY... Canning and Canned Products § 318.310 Personnel and training. All operators of thermal processing systems...

Probing α-3(10) transitions in a voltage-sensing S4 helix.

Science.gov (United States)

Kubota, Tomoya; Lacroix, Jérôme J; Bezanilla, Francisco; Correa, Ana M

2014-09-02

The S4 helix of voltage sensor domains (VSDs) transfers its gating charges across the membrane electrical field in response to changes of the membrane potential. Recent studies suggest that this process may occur via the helical conversion of the entire S4 between α and 310 conformations. Here, using LRET and FRET, we tested this hypothesis by measuring dynamic changes in the transmembrane length of S4 from engineered VSDs expressed in Xenopus oocytes. Our results suggest that the native S4 from the Ciona intestinalis voltage-sensitive phosphatase (Ci-VSP) does not exhibit extended and long-lived 310 conformations and remains mostly α-helical. Although the S4 of NavAb displays a fully extended 310 conformation in x-ray structures, its transplantation in the Ci-VSP VSD scaffold yielded similar results as the native Ci-VSP S4. Taken together, our study does not support the presence of long-lived extended α-to-310 helical conversions of the S4 in Ci-VSP associated with voltage activation. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

32 CFR 310.34 - Amendment and deletion of system notices.

Science.gov (United States)

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false Amendment and deletion of system notices. 310.34... (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Publication Requirements § 310.34 Amendment and deletion of... system. (see § 310.32(q)). (c) Deletion of system notices. (1) Whenever a system is discontinued...

5 CFR 430.310 - Performance Review Boards (PRBs).

Science.gov (United States)

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Performance Review Boards (PRBs). 430.310... PERFORMANCE MANAGEMENT Managing Senior Executive Performance § 430.310 Performance Review Boards (PRBs). Each... appraisal. (3) When appraising a career appointee's performance or recommending a career appointee for a...

15 CFR 310.1 - Background and purpose.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Background and purpose. 310.1 Section 310.1 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S. GOVERNMENT...

41 CFR 101-26.310 - Ordering errors.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 2 2010-07-01 2010-07-01 true Ordering errors. 101-26.310 Section 101-26.310 Public Contracts and Property Management Federal Property Management Regulations System FEDERAL PROPERTY MANAGEMENT REGULATIONS SUPPLY AND PROCUREMENT 26-PROCUREMENT SOURCES AND...

31 CFR 596.310 - Terrorism List Government.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false Terrorism List Government. 596.310... OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY TERRORISM LIST GOVERNMENTS SANCTIONS REGULATIONS General Definitions § 596.310 Terrorism List Government. The term Terrorism List Government...

time series modeling of daily abandoned calls in a call centre

African Journals Online (AJOL)

DJFLEX

Models for evaluating and predicting the short periodic time series in daily ... Ugwuowo (2006) proposed asymmetric angular- linear multivariate regression models, ..... Using the parameter estimates in Table 3, the fitted Fourier series model is ..... For the SARIMA model with the stochastic component also being white noise, ...

41 CFR 109-45.310 - Antitrust laws.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 3 2010-07-01 2010-07-01 false Antitrust laws. 109-45.310 Section 109-45.310 Public Contracts and Property Management Federal Property Management... Antitrust laws. DOE offices shall submit to the Deputy Assistant Secretary for Procurement and Assistance...

Lag space estimation in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...

29 CFR 3.10 - Methods of payment of wages.

Science.gov (United States)

2010-07-01

... 29 Labor 1 2010-07-01 2010-07-01 true Methods of payment of wages. 3.10 Section 3.10 Labor Office... IN WHOLE OR IN PART BY LOANS OR GRANTS FROM THE UNITED STATES § 3.10 Methods of payment of wages. The payment of wages shall be by cash, negotiable instruments payable on demand, or the additional forms of...

32 CFR 310.51 - General.

Science.gov (United States)

2010-07-01

... PROGRAM Computer Matching Program Procedures § 310.51 General. (a) A computer matching program covers two... action against specific individuals. (2) Performed to support research or statistical projects...

Dynamics and deformability of α-, 310- and π-helices

Directory of Open Access Journals (Sweden)

Narwani Tarun Jairaj

2018-01-01

Full Text Available Protein structures are often represented as seen in crystals as (i rigid macromolecules (ii with helices, sheets and coils. However, both definitions are partial because (i proteins are highly dynamic macromolecules and (ii the description of protein structures could be more precise. With regard to these two points, we analyzed and quantified the stability of helices by considering α-helices as well as 310- and π-helices. Molecular dynamic (MD simulations were performed on a large set of 169 representative protein domains. The local protein conformations were followed during each simulation and analyzed. The classical flexibility index (B-factor was confronted with the MD root mean square flexibility (RMSF index. Helical regions were classified according to their level of helicity from high to none. For the first time, a precise quantification showed the percentage of rigid and flexible helices that underlie unexpected behaviors. Only 76.4% of the residues associated with α-helices retain the conformation, while this tendency drops to 40.5% for 310-helices and is never observed for π-helices. α-helix residues that do not remain as an α-helix have a higher tendency to assume β-turn conformations than 310- or π-helices. The 310-helices that switch to the α-helix conformation have a higher B-factor and RMSF values than the average 310-helix but are associated with a lower accessibility. Rare π-helices assume a β-turn, bend and coil conformations, but not α- or 310-helices. The view on π-helices drastically changes with the new DSSP (Dictionary of Secondary Structure of Proteins assignment approach, leading to behavior similar to 310-helices, thus underlining the importance of secondary structure assignment methods.

40 CFR 63.310 - Requirements for startups, shutdowns, and malfunctions.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 9 2010-07-01 2010-07-01 false Requirements for startups, shutdowns, and malfunctions. 63.310 Section 63.310 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... CATEGORIES National Emission Standards for Coke Oven Batteries § 63.310 Requirements for startups, shutdowns...

Time series modeling by a regression approach based on a latent process.

Science.gov (United States)

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Forecasting with nonlinear time series models

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...... and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

Science.gov (United States)

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2017-11-01

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

Modelling conditional heteroscedasticity in nonstationary series

NARCIS (Netherlands)

Cizek, P.; Cizek, P.; Härdle, W.K.; Weron, R.

2011-01-01

A vast amount of econometrical and statistical research deals with modeling financial time series and their volatility, which measures the dispersion of a series at a point in time (i.e., conditional variance). Although financial markets have been experiencing many shorter and longer periods of

Long Memory Models to Generate Synthetic Hydrological Series

Directory of Open Access Journals (Sweden)

Guilherme Armando de Almeida Pereira

2014-01-01

Full Text Available In Brazil, much of the energy production comes from hydroelectric plants whose planning is not trivial due to the strong dependence on rainfall regimes. This planning is accomplished through optimization models that use inputs such as synthetic hydrologic series generated from the statistical model PAR(p (periodic autoregressive. Recently, Brazil began the search for alternative models able to capture the effects that the traditional model PAR(p does not incorporate, such as long memory effects. Long memory in a time series can be defined as a significant dependence between lags separated by a long period of time. Thus, this research develops a study of the effects of long dependence in the series of streamflow natural energy in the South subsystem, in order to estimate a long memory model capable of generating synthetic hydrologic series.

15 CFR 310.7 - Statement for Federal participation.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Statement for Federal participation. 310.7 Section 310.7 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S...

Empirical investigation on modeling solar radiation series with ARMA–GARCH models

International Nuclear Information System (INIS)

Sun, Huaiwei; Yan, Dong; Zhao, Na; Zhou, Jianzhong

2015-01-01

Highlights: • Apply 6 ARMA–GARCH(-M) models to model and forecast solar radiation. • The ARMA–GARCH(-M) models produce more accurate radiation forecasting than conventional methods. • Show that ARMA–GARCH-M models are more effective for forecasting solar radiation mean and volatility. • The ARMA–EGARCH-M is robust and the ARMA–sGARCH-M is very competitive. - Abstract: Simulation of radiation is one of the most important issues in solar utilization. Time series models are useful tools in the estimation and forecasting of solar radiation series and their changes. In this paper, the effectiveness of autoregressive moving average (ARMA) models with various generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH models are evaluated for their effectiveness in radiation series. Six different GARCH approaches, which contain three different ARMA–GARCH models and corresponded GARCH in mean (ARMA–GARCH-M) models, are applied in radiation data sets from two representative climate stations in China. Multiple evaluation metrics of modeling sufficiency are used for evaluating the performances of models. The results show that the ARMA–GARCH(-M) models are effective in radiation series estimation. Both in fitting and prediction of radiation series, the ARMA–GARCH(-M) models show better modeling sufficiency than traditional models, while ARMA–EGARCH-M models are robustness in two sites and the ARMA–sGARCH-M models appear very competitive. Comparisons of statistical diagnostics and model performance clearly show that the ARMA–GARCH-M models make the mean radiation equations become more sufficient. It is recommended the ARMA–GARCH(-M) models to be the preferred method to use in the modeling of solar radiation series

A novel approach for rapid screening of mitochondrial D310 polymorphism

International Nuclear Information System (INIS)

Aral, Cenk; Kaya, Handan; Ataizi-Çelikel, Çiğdem; Akkiprik, Mustafa; Sönmez, Özgür; Güllüoğlu, Bahadır M; Özer, Ayşe

2006-01-01

Mutations in the mitochondrial DNA (mtDNA) have been reported in a wide variety of human neoplasms. A polynucleotide tract extending from 303 to 315 nucleotide positions (D310) within the non-coding region of mtDNA has been identified as a mutational hotspot of primary tumors. This region consists of two polycytosine stretches interrupted by a thymidine nucleotide. The number of cytosines at the first and second stretches are 7 and 5 respectively, according to the GeneBank sequence. The first stretch exhibits a polymorphic length variation (6-C to 9-C) among individuals and has been investigated in many cancer types. Large-scale studies are needed to clarify the relationship between cytosine number and cancer development/progression. However, time and money consuming methods such as radioactivity-based gel electrophoresis and sequencing, are not appropriate for the determination of this polymorphism for large case-control studies. In this study, we conducted a rapid RFLP analysis using a restriction enzyme, BsaXI, for the single step simple determination of 7-C carriers at the first stretch in D310 region. 25 colorectal cancer patients, 25 breast cancer patients and 41 healthy individuals were enrolled into the study. PCR amplification followed by restriction enzyme digestion of D310 region was performed for RFLP analysis. Digestion products were analysed by agarose gel electrophoresis. Sequencing was also applied to samples in order to confirm the RFLP data. Samples containing 7-C at first stretch of D310 region were successfully determined by the BsaXI RFLP method. Heteroplasmy and homoplasmy for 7-C content was also determined as evidenced by direct sequencing. Forty-one percent of the studied samples were found to be BsaXI positive. Furthermore, BsaXI status of colorectal cancer samples were significantly different from that of healthy individuals. In conclusion, BsaXI RFLP analysis is a simple and rapid approach for the single step determination of D310

15 CFR 310.6 - Recognition by the President.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Recognition by the President. 310.6 Section 310.6 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S. GOVERNMENT...

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

Science.gov (United States)

Stránský, V; Thinová, L

2017-11-01

In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Active immunization with the peptide epitope vaccine Aβ3-10-KLH induces a Th2-polarized anti-Aβ antibody response and decreases amyloid plaques in APP/PS1 transgenic mice.

Science.gov (United States)

Ding, Li; Meng, Yuan; Zhang, Hui-Yi; Yin, Wen-Chao; Yan, Yi; Cao, Yun-Peng

2016-11-10

Active amyloid-β (Aβ) immunotherapy is effective in preventing Aβ deposition, facilitating plaque clearance, and improving cognitive functions in mouse models of Alzheimer's disease (AD). Developing a safe and effective AD vaccine requires a delicate balance between inducing adequate humoral immune responses and avoiding T cell-mediated autoimmune responses. In this study, we designed 2 peptide epitope vaccines, Aβ3-10-KLH and 5Aβ3-10, prepared respectively by coupling Aβ3-10 to the immunogenic carrier protein keyhole limpet hemocyanin (KLH) or by joining 5 Aβ3-10 epitopes linearly in tandem. Young APP/PS1 mice were immunized subcutaneously with Aβ3-10-KLH or 5Aβ3-10 mixed with Freund's adjuvant, and the immunopotencies of these Aβ3-10 peptide vaccines were tested. Aβ3-10-KLH elicited a robust Th2-polarized anti-Aβ antibody response and inhibited Aβ deposition in APP/PS1 mice. However, 5Aβ3-10 did not induce an effective humoral immune response. These results indicated that Aβ3-10-KLH may be a safe and efficient vaccine for AD and that conjugating the antigen to a carrier protein may be more effective than linking multiple peptide antigens in tandem in applications for antibody production and vaccine preparation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

Kuznetsov, Vitaly; Mariet, Zelda

2018-01-01

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

33 CFR 334.310 - Chesapeake Bay, Lynnhaven Roads; navy amphibious training area.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 3 2010-07-01 2010-07-01 false Chesapeake Bay, Lynnhaven Roads; navy amphibious training area. 334.310 Section 334.310 Navigation and Navigable Waters CORPS OF....310 Chesapeake Bay, Lynnhaven Roads; navy amphibious training area. (a) The restricted area. Beginning...

Vector bilinear autoregressive time series model and its superiority ...

African Journals Online (AJOL)

In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.

13 CFR 310.2 - Pressing need; alleviation of unemployment or underemployment.

Science.gov (United States)

2010-01-01

... unemployment or underemployment. 310.2 Section 310.2 Business Credit and Assistance ECONOMIC DEVELOPMENT ADMINISTRATION, DEPARTMENT OF COMMERCE SPECIAL IMPACT AREAS § 310.2 Pressing need; alleviation of unemployment or... Special Need. (b) For purposes of this part, excessive unemployment exists if the twenty-four (24) month...

31 CFR 551.310 - U.S. financial institution.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 3 2010-07-01 2010-07-01 false U.S. financial institution. 551.310... Definitions § 551.310 U.S. financial institution. The term U.S. financial institution means any U.S. entity... foregoing. This term includes those branches, offices and agencies of foreign financial institutions that...

Time domain series system definition and gear set reliability modeling

International Nuclear Information System (INIS)

Xie, Liyang; Wu, Ningxiang; Qian, Wenxue

2016-01-01

Time-dependent multi-configuration is a typical feature for mechanical systems such as gear trains and chain drives. As a series system, a gear train is distinct from a traditional series system, such as a chain, in load transmission path, system-component relationship, system functioning manner, as well as time-dependent system configuration. Firstly, the present paper defines time-domain series system to which the traditional series system reliability model is not adequate. Then, system specific reliability modeling technique is proposed for gear sets, including component (tooth) and subsystem (tooth-pair) load history description, material priori/posterior strength expression, time-dependent and system specific load-strength interference analysis, as well as statistically dependent failure events treatment. Consequently, several system reliability models are developed for gear sets with different tooth numbers in the scenario of tooth root material ultimate tensile strength failure. The application of the models is discussed in the last part, and the differences between the system specific reliability model and the traditional series system reliability model are illustrated by virtue of several numerical examples. - Highlights: • A new type of series system, i.e. time-domain multi-configuration series system is defined, that is of great significance to reliability modeling. • Multi-level statistical analysis based reliability modeling method is presented for gear transmission system. • Several system specific reliability models are established for gear set reliability estimation. • The differences between the traditional series system reliability model and the new model are illustrated.

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

Science.gov (United States)

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

forecasting with nonlinear time series model: a monte-carlo

African Journals Online (AJOL)

PUBLICATIONS1

Carlo method of forecasting using a special nonlinear time series model, called logistic smooth transition ... We illustrate this new method using some simulation ..... in MATLAB 7.5.0. ... process (DGP) using the logistic smooth transi-.

33 CFR 160.310 - Definitions.

Science.gov (United States)

2010-07-01

....310 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) PORTS AND... credential; (5) Transportation Worker Identification Credential (TWIC) issued by the Transportation Security... authority of the government of a country that has ratified the International Labour Organization Seafarers...

Pin failure modeling of the A series CABRI tests

International Nuclear Information System (INIS)

Young, M.F.; Portugal, J.L.

1978-01-01

The EXPAND pin fialure model, a research tool designed to model pin failure under prompt burst conditions, has been used to predict failure conditions for several of the A series CABRI tests as part of the United States participation in the CABRI Joint Project. The Project is an international program involving France, Germany, England, Japan, and the United States and has the goal of obtaining experimental data relating to the safety of LMFBR's. The A series, designed to simulate high ramp rate TOP conditions, initially utilizes single, fresh UO 2 pins of the PHENIX type in a flowing sodium loop. The pins are preheated at constant power in the CABRI reactor to establish steady state conditions (480 w/cm at the axial peak) and then subjected to a power pulse of 14 ms to 24 ms duration

A time series model: First-order integer-valued autoregressive (INAR(1))

Science.gov (United States)

Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

2017-07-01

Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

40 CFR 142.310 - How can a person served by the public water system obtain EPA review of a State proposed small...

Science.gov (United States)

2010-07-01

... water system obtain EPA review of a State proposed small system variance? 142.310 Section 142.310... PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System Public Participation § 142.310 How can a person served by the public water system obtain EPA review of a State proposed small...

Modeling seasonality in bimonthly time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans)

1992-01-01

textabstractA recurring issue in modeling seasonal time series variables is the choice of the most adequate model for the seasonal movements. One selection method for quarterly data is proposed in Hylleberg et al. (1990). Market response models are often constructed for bimonthly variables, and

Modelling road accidents: An approach using structural time series

Science.gov (United States)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

75 FR 39863 - Airworthiness Directives; Airbus Model A310 Series Airplanes

Science.gov (United States)

2010-07-13

... condition as: Analysis performed in the frame of the Extended Service Goal has led Airbus to modify the... performed in the frame of the Extended Service Goal has led Airbus to modify the inspection programme...) states: Analysis performed in the frame of the Extended Service Goal has led Airbus to modify the...

76 FR 27227 - Airworthiness Directives; Airbus Model A310 Series Airplanes

Science.gov (United States)

2011-05-11

... program, EASA [European Aviation Safety Agency] published AD 2007-0053, which replaced DGAC France AD F... airplane configuration: Cold working of trellis boom drainage holes. Repetitive detailed or rotating probe... take about 137 work-hours per product to comply with the basic requirements of this AD. The average...

76 FR 27220 - Airworthiness Directives; Airbus Model A310 Series Airplanes

Science.gov (United States)

2011-05-11

... cracking in certain bolt holes where the main landing gear forward pick-up fitting is attached to the rear... labor rate is $85 per work-hour. Based on these figures, we estimate the cost of this AD to the U.S... government and the States, or on the distribution of power and responsibilities among the various levels of...

76 FR 27232 - Airworthiness Directives; Airbus Model A310 Series Airplanes

Science.gov (United States)

2011-05-11

... directive (AD) for the products listed above that would supersede two existing ADs. This AD results from mandatory continuing airworthiness information (MCAI) originated by an aviation authority of another country... product. Authority for This Rulemaking Title 49 of the United States Code specifies the FAA's authority to...

76 FR 42 - Airworthiness Directives; Airbus Model A310 Series Airplanes

Science.gov (United States)

2011-01-03

... may send comments by any of the following methods: Federal eRulemaking Portal: Go to http://www... 1992-106-132(B)R4, dated June 5, 1996]. Following the Extended Design Service Goal activities part of... inspection to detect cracks in the area of frame 47 and frame 54, install new doublers, and repair if...

15 CFR 310.8 - Proposed plan for Federal participation.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Proposed plan for Federal participation. 310.8 Section 310.8 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S...

40 CFR 310.24 - What happens if I provide incorrect or false information?

Science.gov (United States)

2010-07-01

... false information? 310.24 Section 310.24 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... § 310.24 What happens if I provide incorrect or false information? (a) You must not knowingly or recklessly make any statement or provide any information in your reimbursement application that is false...

45 CFR 310.1 - What definitions apply to this part?

Science.gov (United States)

2010-10-01

... SERVICES COMPUTERIZED TRIBAL IV-D SYSTEMS AND OFFICE AUTOMATION General Provisions § 310.1 What definitions... 45 Public Welfare 2 2010-10-01 2010-10-01 false What definitions apply to this part? 310.1 Section...) Office Automation means a generic adjunct component of a computer system that supports the routine...

19 CFR 10.310 - Election to average for motor vehicles.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 1 2010-04-01 2010-04-01 false Election to average for motor vehicles. 10.310... Free Trade Agreement § 10.310 Election to average for motor vehicles. (a) Election. In determining whether a motor vehicle is originating for purposes of the preferences under the Agreement or a Canadian...

Analysis of Data from a Series of Events by a Geometric Process Model

Institute of Scientific and Technical Information of China (English)

Yeh Lam; Li-xing Zhu; Jennifer S. K. Chan; Qun Liu

2004-01-01

Geometric process was first introduced by Lam[10,11]. A stochastic process {Xi, i = 1, 2,…} is called a geometric process (GP) if, for some a > 0, {ai-1Xi, i = 1, 2,…} forms a renewal process. In thispaper, the GP is used to analyze the data from a series of events. A nonparametric method is introduced forthe estimation of the three parameters in the GP. The limiting distributions of the three estimators are studied.Through the analysis of some real data sets, the GP model is compared with other three homogeneous andnonhomogeneous Poisson models. It seems that on average the GP model is the best model among these fourmodels in analyzing the data from a series of events.

Modeling sports highlights using a time-series clustering framework and model interpretation

Science.gov (United States)

Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay

2005-01-01

In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.

A prediction method based on wavelet transform and multiple models fusion for chaotic time series

International Nuclear Information System (INIS)

Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha

2017-01-01

In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect

OpenAIRE

Yanhui Xi; Hui Peng; Yemei Qin

2016-01-01

The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation....

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

Science.gov (United States)

Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

2017-04-01

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

46 CFR 310.52 - General.

Science.gov (United States)

2010-10-01

..., are required by the Act to sign an agreement committing them to service obligations following the date of graduation. The terms of the service obligation contract are set forth in § 310.58 of this subpart. ...

Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective

CERN Document Server

Chen, Shyi-Ming

2013-01-01

Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological a...

21 CFR 1404.310 - What must I do if a Federal agency excludes a person with whom I am already doing business in a...

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 9 2010-04-01 2010-04-01 false What must I do if a Federal agency excludes a person with whom I am already doing business in a covered transaction? 1404.310 Section 1404.310 Food and...) Responsibilities of Participants Regarding Transactions Doing Business with Other Persons § 1404.310 What must I do...

16 CFR 310.6 - Exemptions.

Science.gov (United States)

2010-01-01

...), and (c); (2) The sale of franchises subject to the Commission's Rule entitled “Disclosure Requirements and Prohibitions Concerning Franchising and Business Opportunity Ventures,” (“Franchise Rule”) 16 CFR... covered by the Franchise Rule, or advertisements involving goods or services described in §§ 310.3(a)(1...

A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series

Directory of Open Access Journals (Sweden)

Fernando Luiz Cyrino Oliveira

2014-01-01

Full Text Available The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(p, one for each period (month of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “p” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR(p models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning.

The Exponential Model for the Spectrum of a Time Series: Extensions and Applications

DEFF Research Database (Denmark)

Proietti, Tommaso; Luati, Alessandra

The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time...

Volterra-series-based nonlinear system modeling and its engineering applications: A state-of-the-art review

Science.gov (United States)

Cheng, C. M.; Peng, Z. K.; Zhang, W. M.; Meng, G.

2017-03-01

Nonlinear problems have drawn great interest and extensive attention from engineers, physicists and mathematicians and many other scientists because most real systems are inherently nonlinear in nature. To model and analyze nonlinear systems, many mathematical theories and methods have been developed, including Volterra series. In this paper, the basic definition of the Volterra series is recapitulated, together with some frequency domain concepts which are derived from the Volterra series, including the general frequency response function (GFRF), the nonlinear output frequency response function (NOFRF), output frequency response function (OFRF) and associated frequency response function (AFRF). The relationship between the Volterra series and other nonlinear system models and nonlinear problem solving methods are discussed, including the Taylor series, Wiener series, NARMAX model, Hammerstein model, Wiener model, Wiener-Hammerstein model, harmonic balance method, perturbation method and Adomian decomposition. The challenging problems and their state of arts in the series convergence study and the kernel identification study are comprehensively introduced. In addition, a detailed review is then given on the applications of Volterra series in mechanical engineering, aeroelasticity problem, control engineering, electronic and electrical engineering.

Oilfield automation with MRD-310 industrial 3G router

Energy Technology Data Exchange (ETDEWEB)

Anon.

2010-01-15

The SAM Well Manager is the most advanced technological solution for high precision monitoring and control of rod-pumping wells. It was developed by Lufkin-Automation to optimize pump performance and reliability. The company was formed by the merger of Delta X and Nabla Corporations, which are currently part of the oil field division, offering analysis, security and control solutions for rod pumping wells. The SAM Well Manager product is equipped with the Westermo MRD-310 3G router. It has been used in pilot projects in remote and harsh environments in Europe and Asia. The MRD-310 router has a very high level of connectivity and provides support for GSM, GPRS, 3G UMTS, HSDPA and HSUPA. It also supports IPSec encrypted VPN tunnels which is a requirement for safety critical applications that use unsecure public networks. In addition, the MRD-310 provides a serial interface and serial to IP conversion, which is required for connection to the controller. The router is used to analyze pump flow and calculate service and maintenance requirements before the event of mechanical failure. Energy consumption can also be optimized. This installation of the SAM Well Manager with MRD-310 industrial 3G router in several applications has demonstrated the potential for significant cost savings. 2 figs.

Improved time series prediction with a new method for selection of model parameters

International Nuclear Information System (INIS)

Jade, A M; Jayaraman, V K; Kulkarni, B D

2006-01-01

A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

Protection against wing icing for Airbus A300 and A310

Science.gov (United States)

Woelfer, G.

1981-01-01

To improve economy of operation, it is now planned to modify the anti-icing system used on the A300 Airbus wing. Thus, for the A310 Airbus, the deicing system will be applied to only half the wing length. Other essential modifications are a substantial simplification of the warm-air system and discontinuation of the use of a double wall in slats.

10 CFR 603.310 - Use of an expenditure-based TIA.

Science.gov (United States)

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Use of an expenditure-based TIA. 603.310 Section 603.310 Energy DEPARTMENT OF ENERGY (CONTINUED) ASSISTANCE REGULATIONS TECHNOLOGY INVESTMENT AGREEMENTS... expenditure-based TIA. In general, the contracting officer must use an expenditure-based TIA under conditions...

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

Parameterizing unconditional skewness in models for financial time series

DEFF Research Database (Denmark)

He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo

In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate...

Koopman Operator Framework for Time Series Modeling and Analysis

Science.gov (United States)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Trend time-series modeling and forecasting with neural networks.

Science.gov (United States)

Qi, Min; Zhang, G Peter

2008-05-01

Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

Estimation of pure autoregressive vector models for revenue series ...

African Journals Online (AJOL)

This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...

10 CFR 600.310 - Purpose of financial and program management.

Science.gov (United States)

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Purpose of financial and program management. 600.310... Requirements § 600.310 Purpose of financial and program management. Sections 600.311 through 600.318 prescribe standards for financial management systems; methods for making payments; and rules for cost sharing and...

Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events

DEFF Research Database (Denmark)

Madsen, Henrik; Rasmussen, Peter F.; Rosbjerg, Dan

1997-01-01

Two different models for analyzing extreme hydrologic events, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto distribution for modeling threshold exceedances corresponding to a generalized extreme value......). In the case of ML estimation, the PDS model provides the most efficient T-year event estimator. In the cases of MOM and PWM estimation, the PDS model is generally preferable for negative shape parameters, whereas the AMS model yields the most efficient estimator for positive shape parameters. A comparison...... of the considered methods reveals that in general, one should use the PDS model with MOM estimation for negative shape parameters, the PDS model with exponentially distributed exceedances if the shape parameter is close to zero, the AMS model with MOM estimation for moderately positive shape parameters, and the PDS...

42 CFR 57.310 - Repayment and collection of nursing student loans.

Science.gov (United States)

2010-10-01

... exercise of due diligence, a school must follow procedures which are at least as extensive and effective as... 42 Public Health 1 2010-10-01 2010-10-01 false Repayment and collection of nursing student loans. 57.310 Section 57.310 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES...

38 CFR 41.310 - Financial statements.

Science.gov (United States)

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Financial statements. 41...) AUDITS OF STATES, LOCAL GOVERNMENTS, AND NON-PROFIT ORGANIZATIONS Auditees § 41.310 Financial statements. (a) Financial statements. The auditee shall prepare financial statements that reflect its financial...

16 CFR 310.7 - Actions by states and private persons.

Science.gov (United States)

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Actions by states and private persons. 310.7 Section 310.7 Commercial Practices FEDERAL TRADE COMMISSION REGULATIONS UNDER SPECIFIC ACTS OF CONGRESS... complaint and any other pleadings to be filed with the court. If prior notice is not feasible, the state or...

Sensitivity analysis of machine-learning models of hydrologic time series

Science.gov (United States)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

A COMPARATIVE STUDY OF FORECASTING MODELS FOR TREND AND SEASONAL TIME SERIES DOES COMPLEX MODEL ALWAYS YIELD BETTER FORECAST THAN SIMPLE MODELS

Directory of Open Access Journals (Sweden)

Suhartono Suhartono

2005-01-01

Full Text Available Many business and economic time series are non-stationary time series that contain trend and seasonal variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, or repeating promotions. A stochastic trend is often accompanied with the seasonal variations and can have a significant impact on various forecasting methods. In this paper, we will investigate and compare some forecasting methods for modeling time series with both trend and seasonal patterns. These methods are Winter's, Decomposition, Time Series Regression, ARIMA and Neural Networks models. In this empirical research, we study on the effectiveness of the forecasting performance, particularly to answer whether a complex method always give a better forecast than a simpler method. We use a real data, that is airline passenger data. The result shows that the more complex model does not always yield a better result than a simpler one. Additionally, we also find the possibility to do further research especially the use of hybrid model by combining some forecasting method to get better forecast, for example combination between decomposition (as data preprocessing and neural network model.

A new model for reliability optimization of series-parallel systems with non-homogeneous components

International Nuclear Information System (INIS)

Feizabadi, Mohammad; Jahromi, Abdolhamid Eshraghniaye

2017-01-01

In discussions related to reliability optimization using redundancy allocation, one of the structures that has attracted the attention of many researchers, is series-parallel structure. In models previously presented for reliability optimization of series-parallel systems, there is a restricting assumption based on which all components of a subsystem must be homogeneous. This constraint limits system designers in selecting components and prevents achieving higher levels of reliability. In this paper, a new model is proposed for reliability optimization of series-parallel systems, which makes possible the use of non-homogeneous components in each subsystem. As a result of this flexibility, the process of supplying system components will be easier. To solve the proposed model, since the redundancy allocation problem (RAP) belongs to the NP-hard class of optimization problems, a genetic algorithm (GA) is developed. The computational results of the designed GA are indicative of high performance of the proposed model in increasing system reliability and decreasing costs. - Highlights: • In this paper, a new model is proposed for reliability optimization of series-parallel systems. • In the previous models, there is a restricting assumption based on which all components of a subsystem must be homogeneous. • The presented model provides a possibility for the subsystems’ components to be non- homogeneous in the required conditions. • The computational results demonstrate the high performance of the proposed model in improving reliability and reducing costs.

Stochastic modeling of hourly rainfall times series in Campania (Italy)

Science.gov (United States)

Giorgio, M.; Greco, R.

2009-04-01

Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil

A regional and nonstationary model for partial duration series of extreme rainfall

DEFF Research Database (Denmark)

Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan

2017-01-01

as the explanatory variables in the regional and temporal domain, respectively. Further analysis of partial duration series with nonstationary and regional thresholds shows that the mean exceedances also exhibit a significant variation in space and time for some rainfall durations, while the shape parameter is found...... of extreme rainfall. The framework is built on a partial duration series approach with a nonstationary, regional threshold value. The model is based on generalized linear regression solved by generalized estimation equations. It allows a spatial correlation between the stations in the network and accounts...... furthermore for variable observation periods at each station and in each year. Marginal regional and temporal regression models solved by generalized least squares are used to validate and discuss the results of the full spatiotemporal model. The model is applied on data from a large Danish rain gauge network...

Series-NonUniform Rational B-Spline (S-NURBS) model: a geometrical interpolation framework for chaotic data.

Science.gov (United States)

Shao, Chenxi; Liu, Qingqing; Wang, Tingting; Yin, Peifeng; Wang, Binghong

2013-09-01

Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible.

Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction

Directory of Open Access Journals (Sweden)

Helmut Thome

2015-07-01

Full Text Available Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality. To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other. The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling.

Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

Science.gov (United States)

Soloway, Donald I.; Bialasiewicz, Jan T.

1992-01-01

A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.

Science.gov (United States)

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

Modeling vector nonlinear time series using POLYMARS

NARCIS (Netherlands)

de Gooijer, J.G.; Ray, B.K.

2003-01-01

A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector

75 FR 49370 - Airworthiness Directives; Airbus Model A300 B4-600, B4-600R, and F4-600R Series Airplanes, and...

Science.gov (United States)

2010-08-13

... cycling and that a previous study on a Model A310 airplane had shown no reduction in stiffness due to age... considers the equipment for both inspections to be special tooling and the inspection methods to be of... of civil aircraft in air commerce by prescribing regulations for practices, methods, and procedures...

Outlier Detection in Structural Time Series Models

DEFF Research Database (Denmark)

Marczak, Martyna; Proietti, Tommaso

investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...

Modelling bursty time series

International Nuclear Information System (INIS)

Vajna, Szabolcs; Kertész, János; Tóth, Bálint

2013-01-01

Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (β) and autocorrelation function (α): α + β = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated. (paper)

Models for Pooled Time-Series Cross-Section Data

Directory of Open Access Journals (Sweden)

Lawrence E Raffalovich

2015-07-01

Full Text Available Several models are available for the analysis of pooled time-series cross-section (TSCS data, defined as “repeated observations on fixed units” (Beck and Katz 1995. In this paper, we run the following models: (1 a completely pooled model, (2 fixed effects models, and (3 multi-level/hierarchical linear models. To illustrate these models, we use a Generalized Least Squares (GLS estimator with cross-section weights and panel-corrected standard errors (with EViews 8 on the cross-national homicide trends data of forty countries from 1950 to 2005, which we source from published research (Messner et al. 2011. We describe and discuss the similarities and differences between the models, and what information each can contribute to help answer substantive research questions. We conclude with a discussion of how the models we present may help to mitigate validity threats inherent in pooled time-series cross-section data analysis.

Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit

OpenAIRE

Miroslaw Luft; Elzbieta Szychta

2008-01-01

The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

Science.gov (United States)

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

A stochastic HMM-based forecasting model for fuzzy time series.

Science.gov (United States)

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

Multiple Time Series Ising Model for Financial Market Simulations

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2015-01-01

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

Mathematical model of thyristor inverter including a series-parallel resonant circuit

OpenAIRE

Luft, M.; Szychta, E.

2008-01-01

The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with the aid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.

Forecasting the Reference Evapotranspiration Using Time Series Model

Directory of Open Access Journals (Sweden)

H. Zare Abyaneh

2016-10-01

Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference

The First European Parabolic Flight Campaign with the Airbus A310 ZERO-G

Science.gov (United States)

Pletser, Vladimir; Rouquette, Sebastien; Friedrich, Ulrike; Clervoy, Jean-Francois; Gharib, Thierry; Gai, Frederic; Mora, Christophe

2016-12-01

Aircraft parabolic flights repetitively provide up to 23 seconds of reduced gravity during ballistic flight manoeuvres. Parabolic flights are used to conduct short microgravity investigations in Physical and Life Sciences and in Technology, to test instrumentation prior to space flights and to train astronauts before a space mission. The use of parabolic flights is complementary to other microgravity carriers (drop towers, sounding rockets), and preparatory to manned space missions on board the International Space Station and other manned spacecraft, such as Shenzhou and the future Chinese Space Station. After 17 years of using the Airbus A300 ZERO-G, the French company Novespace, a subsidiary of the ' Centre National d'Etudes Spatiales' (CNES, French Space Agency), based in Bordeaux, France, purchased a new aircraft, an Airbus A310, to perform parabolic flights for microgravity research in Europe. Since April 2015, the European Space Agency (ESA), CNES and the ` Deutsches Zentrum für Luft- und Raumfahrt e.V.' (DLR, the German Aerospace Center) use this new aircraft, the Airbus A310 ZERO-G, for research experiments in microgravity. The first campaign was a Cooperative campaign shared by the three agencies, followed by respectively a CNES, an ESA and a DLR campaign. This paper presents the new Airbus A310 ZERO-G and its main characteristics and interfaces for scientific experiments. The experiments conducted during the first European campaign are presented.

33 CFR 103.310 - Responsibilities of the Area Maritime Security (AMS) Committee.

Science.gov (United States)

2010-07-01

... disseminating appropriate security information to port stakeholders. ... Maritime Security (AMS) Committee. 103.310 Section 103.310 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY MARITIME SECURITY MARITIME SECURITY: AREA MARITIME SECURITY Area Maritime...

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

Science.gov (United States)

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682

15 CFR 310.9 - Report of the Secretary on Federal participation.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Report of the Secretary on Federal participation. 310.9 Section 310.9 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S...

15 CFR 310.5 - Report of the Secretary on Federal recognition.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Report of the Secretary on Federal recognition. 310.5 Section 310.5 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S...

20 CFR 670.310 - How are entities selected to receive funding?

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false How are entities selected to receive funding? 670.310 Section 670.310 Employees' Benefits EMPLOYMENT AND TRAINING ADMINISTRATION, DEPARTMENT OF LABOR THE JOB CORPS UNDER TITLE I OF THE WORKFORCE INVESTMENT ACT Funding and Selection of Service...

Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit

Directory of Open Access Journals (Sweden)

Miroslaw Luft

2008-01-01

Full Text Available The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.

FOURIER SERIES MODELS THROUGH TRANSFORMATION

African Journals Online (AJOL)

DEPT

monthly temperature data (1996 – 2005) collected from the National Root ... KEY WORDS: Fourier series, square transformation, multiplicative model, ... fluctuations or movements are often periodic(Ekpeyong,2005). .... significant trend or not, if the trend is not significant, the grand mean may be used as an estimate of trend.

7 CFR 900.310 - Supplementary instructions.

Science.gov (United States)

2010-01-01

... Conduct of Referenda To Determine Producer Approval of Milk Marketing Orders To Be Made Effective Pursuant to Agricultural Marketing Agreement Act of 1937, as Amended § 900.310 Supplementary instructions. The... Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing...

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Science.gov (United States)

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Directory of Open Access Journals (Sweden)

Ching-Hsue Cheng

2018-01-01

Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

25 CFR 171.310 - Can I use water delivered by BIA for livestock purposes?

Science.gov (United States)

2010-04-01

... 25 Indians 1 2010-04-01 2010-04-01 false Can I use water delivered by BIA for livestock purposes? 171.310 Section 171.310 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT OF THE INTERIOR LAND AND WATER IRRIGATION OPERATION AND MAINTENANCE Water Use § 171.310 Can I use water delivered by BIA for livestock...

21 CFR 201.310 - Phenindione; labeling of drug preparations intended for use by man.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 4 2010-04-01 2010-04-01 false Phenindione; labeling of drug preparations intended for use by man. 201.310 Section 201.310 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL LABELING Specific Labeling Requirements for Specific Drug Products § 201.310 Phenindione;...

The use of synthetic input sequences in time series modeling

International Nuclear Information System (INIS)

Oliveira, Dair Jose de; Letellier, Christophe; Gomes, Murilo E.D.; Aguirre, Luis A.

2008-01-01

In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

Science.gov (United States)

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

20 CFR 1010.310 - How will priority of service be applied?

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false How will priority of service be applied? 1010.310 Section 1010.310 Employees' Benefits OFFICE OF THE ASSISTANT SECRETARY FOR VETERANS' EMPLOYMENT... covered persons at the point of entry, whether in person or virtual, so the covered person can be notified...

21 CFR 172.310 - Aluminum nicotinate.

Science.gov (United States)

2010-04-01

... and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES PERMITTED FOR DIRECT ADDITION TO FOOD FOR HUMAN CONSUMPTION Special Dietary and Nutritional Additives § 172.310 Aluminum nicotinate. Aluminum nicotinate may be safely...

7 CFR 3017.310 - What must I do if a Federal agency excludes a person with whom I am already doing business in a...

Science.gov (United States)

2010-01-01

... with whom I am already doing business in a covered transaction? 3017.310 Section 3017.310 Agriculture... already doing business in a covered transaction? (a) You as a participant may continue covered transactions with an excluded person if the transactions were in existence when the agency excluded the person...

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

Science.gov (United States)

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

Evaluation of nonlinearity and validity of nonlinear modeling for complex time series.

Science.gov (United States)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2007-10-01

Even if an original time series exhibits nonlinearity, it is not always effective to approximate the time series by a nonlinear model because such nonlinear models have high complexity from the viewpoint of information criteria. Therefore, we propose two measures to evaluate both the nonlinearity of a time series and validity of nonlinear modeling applied to it by nonlinear predictability and information criteria. Through numerical simulations, we confirm that the proposed measures effectively detect the nonlinearity of an observed time series and evaluate the validity of the nonlinear model. The measures are also robust against observational noises. We also analyze some real time series: the difference of the number of chickenpox and measles patients, the number of sunspots, five Japanese vowels, and the chaotic laser. We can confirm that the nonlinear model is effective for the Japanese vowel /a/, the difference of the number of measles patients, and the chaotic laser.

Estimating High-Dimensional Time Series Models

DEFF Research Database (Denmark)

Medeiros, Marcelo C.; Mendes, Eduardo F.

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly......, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows...

Modeling of plasma chemistry in a corona streamer pulse series in air

International Nuclear Information System (INIS)

Nowakowska, H.; Stanco, J.; Dors, M.; Mizeraczyk, J.

2002-01-01

The aim of this study is to analyse the chemistry in air treated by a series of corona discharge streamers. Attention is focused on the conversion of ozone and nitrogen oxides. In the model it is assumed that the streamer head of relatively small geometrical dimensions propagates from the anode to the cathode, leaving the streamer channel behind. Any elemental gas volume in the streamer path is subjected first to the conditions of the streamer head, and next to those of the streamer channel. The kinetics of plasma-chemical processes occurring in the gas is modeled numerically for a single streamer and a series of streamers. The temporal evolution of 25 chemical compounds initially present or produced in air is calculated. (author)

Predicting long-term catchment nutrient export: the use of nonlinear time series models

Science.gov (United States)

Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

2010-05-01

After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the

Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Directory of Open Access Journals (Sweden)

Rui Xue

2015-01-01

Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

Evaluation of the autoregression time-series model for analysis of a noisy signal

International Nuclear Information System (INIS)

Allen, J.W.

1977-01-01

The autoregression (AR) time-series model of a continuous noisy signal was statistically evaluated to determine quantitatively the uncertainties of the model order, the model parameters, and the model's power spectral density (PSD). The result of such a statistical evaluation enables an experimenter to decide whether an AR model can adequately represent a continuous noisy signal and be consistent with the signal's frequency spectrum, and whether it can be used for on-line monitoring. Although evaluations of other types of signals have been reported in the literature, no direct reference has been found to AR model's uncertainties for continuous noisy signals; yet the evaluation is necessary to decide the usefulness of AR models of typical reactor signals (e.g., neutron detector output or thermocouple output) and the potential of AR models for on-line monitoring applications. AR and other time-series models for noisy data representation are being investigated by others since such models require fewer parameters than the traditional PSD model. For this study, the AR model was selected for its simplicity and conduciveness to uncertainty analysis, and controlled laboratory bench signals were used for continuous noisy data. (author)

Forecasting with periodic autoregressive time series models

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

1999-01-01

textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

On modeling panels of time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans)

2002-01-01

textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a

New insights into soil temperature time series modeling: linear or nonlinear?

Science.gov (United States)

Bonakdari, Hossein; Moeeni, Hamid; Ebtehaj, Isa; Zeynoddin, Mohammad; Mahoammadian, Abdolmajid; Gharabaghi, Bahram

2018-03-01

Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models' relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and

Rotation in the dynamic factor modeling of multivariate stationary time series.

NARCIS (Netherlands)

Molenaar, P.C.M.; Nesselroade, J.R.

2001-01-01

A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white

Time-series modeling: applications to long-term finfish monitoring data

International Nuclear Information System (INIS)

Bireley, L.E.

1985-01-01

The growing concern and awareness that developed during the 1970's over the effects that industry had on the environment caused the electric utility industry in particular to develop monitoring programs. These programs generate long-term series of data that are not very amenable to classical normal-theory statistical analysis. The monitoring data collected from three finfish programs (impingement, trawl and seine) at the Millstone Nuclear Power Station were typical of such series and thus were used to develop methodology that used the full extent of the information in the series. The basis of the methodology was classic Box-Jenkins time-series modeling; however, the models also included deterministic components that involved flow, season and time as predictor variables. Time entered into the models as harmonic regression terms. Of the 32 models fitted to finfish catch data, 19 were found to account for more than 70% of the historical variation. The models were than used to forecast finfish catches a year in advance and comparisons were made to actual data. Usually the confidence intervals associated with the forecasts encompassed most of the observed data. The technique can provide the basis for intervention analysis in future impact assessments

A time series modeling approach in risk appraisal of violent and sexual recidivism.

Science.gov (United States)

Bani-Yaghoub, Majid; Fedoroff, J Paul; Curry, Susan; Amundsen, David E

2010-10-01

For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.

45 CFR 310.5 - What options are available for Computerized Tribal IV-D Systems and office automation?

Science.gov (United States)

2010-10-01

... IV-D Systems and office automation? 310.5 Section 310.5 Public Welfare Regulations Relating to Public... AUTOMATION Requirements for Computerized Tribal IV-D Systems and Office Automation § 310.5 What options are available for Computerized Tribal IV-D Systems and office automation? (a) Allowable computerized support...

Diffusion coefficients of nickel chloride in aqueous solutions of lactose at T = 298.15 K and T = 310.15 K

International Nuclear Information System (INIS)

Ribeiro, Ana C.F.; Gomes, Joselaine C.S.; Barros, Marisa C.F.; Lobo, Victor M.M.; Esteso, Miguel A.

2011-01-01

Binary mutual diffusion coefficients (interdiffusion coefficients) of nickel chloride in water at T = 298.15 K and T = 310.15 K, and at concentrations between (0.000 and 0.100) mol · dm -3 , using a Taylor dispersion method have been measured. These data are discussed on the basis of the Onsager-Fuoss and Pikal models. The equivalent conductance at infinitesimal concentration of the nickel ion in these solutions at T = 310.15 K has been estimated using these results. Through the same technique, ternary mutual diffusion coefficients (D 11 , D 22 , D 12 , and D 21 ) for aqueous solutions containing NiCl 2 and lactose, at T = 298.15 K and T = 310.15 K, and at different carrier concentrations were also measured. These data permit us to have a better understanding of the structure of these systems and the thermodynamic behaviour of NiCl 2 in different media.

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Directory of Open Access Journals (Sweden)

Dario Pumo

2016-05-01

Full Text Available Many hydrologic applications require reliable estimates of runoff in river basins to face the widespread lack of data, both in time and in space. A regional method for the reconstruction of monthly runoff series is here developed and applied to Sicily (Italy. A simple modeling structure is adopted, consisting of a regression-based rainfall–runoff model with four model parameters, calibrated through a two-step procedure. Monthly runoff estimates are based on precipitation, temperature, and exploiting the autocorrelation with runoff at the previous month. Model parameters are assessed by specific regional equations as a function of easily measurable physical and climate basin descriptors. The first calibration step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such “optimal” sets are used at the second step, part of a regional regression analysis, to establish the regional equations for model parameters assessment as a function of basin attributes. All the gauged watersheds across the region have been analyzed, selecting 53 basins for model calibration and using the other six basins exclusively for validation. Performances, quantitatively evaluated by different statistical indexes, demonstrate relevant model ability in reproducing the observed hydrological time-series at both the monthly and coarser time resolutions. The methodology, which is easily transferable to other arid and semi-arid areas, provides a reliable tool for filling/reconstructing runoff time series at any gauged or ungauged basin of a region.

Normalization of time-series satellite reflectance data to a standard sun-target-sensor geometry using a semi-empirical model

Science.gov (United States)

Zhao, Yongguang; Li, Chuanrong; Ma, Lingling; Tang, Lingli; Wang, Ning; Zhou, Chuncheng; Qian, Yonggang

2017-10-01

Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.

38 CFR 3.10 - Dependency and indemnity compensation rate for a surviving spouse.

Science.gov (United States)

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 1 2010-07-01 2010-07-01 false Dependency and indemnity... OF VETERANS AFFAIRS ADJUDICATION Pension, Compensation, and Dependency and Indemnity Compensation General § 3.10 Dependency and indemnity compensation rate for a surviving spouse. (a) General...

32 CFR 310.14 - Notification when information is lost, stolen, or compromised.

Science.gov (United States)

2010-07-01

... THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Systems of Records § 310.14... the individual of any loss, theft, or compromise (See also, § 310.50 for reporting of the breach to Senior Component Official for Privacy and the Defense Privacy Office). (1) The notification shall be made...

Modelling Changes in the Unconditional Variance of Long Stock Return Series

DEFF Research Database (Denmark)

Amado, Cristina; Teräsvirta, Timo

In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long return series. For the purpose, we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta (2011...... show that the long-memory property in volatility may be explained by ignored changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecast accuracy of the new model over the GJR-GARCH model at all...... horizons for a subset of the long return series....

21 CFR 310.544 - Drug products containing active ingredients offered over-the-counter (OTC) for use as a smoking...

Science.gov (United States)

2010-04-01

... offered over-the-counter (OTC) for use as a smoking deterrent. 310.544 Section 310.544 Food and Drugs FOOD... ingredients offered over-the-counter (OTC) for use as a smoking deterrent. (a) Any product that bears labeling claims that it “helps stop or reduce the cigarette urge,” “helps break the cigarette habit,” “helps stop...

Modelling changes in the unconditional variance of long stock return series

DEFF Research Database (Denmark)

Amado, Cristina; Teräsvirta, Timo

2014-01-01

In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta...... that the apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight...... subsets of the long return series....

Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

Zucchini, Walter

2009-01-01

Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

Science.gov (United States)

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Di Mari, Roberto

2018-01-01

We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

Science.gov (United States)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

Degeneracy of time series models: The best model is not always the correct model

International Nuclear Information System (INIS)

Judd, Kevin; Nakamura, Tomomichi

2006-01-01

There are a number of good techniques for finding, in some sense, the best model of a deterministic system given a time series of observations. We examine a problem called model degeneracy, which has the consequence that even when a perfect model of a system exists, one does not find it using the best techniques currently available. The problem is illustrated using global polynomial models and the theory of Groebner bases

Corrosion resistance of Ni-50Cr HVOF coatings on 310S alloy substrates in a metal dusting atmosphere

Energy Technology Data Exchange (ETDEWEB)

Saaedi, J. [Centre for Advanced Coating Technologies, Department of Materials Science and Engineering, University of Toronto, 184 College Street, Toronto, Ontario M5S 3E4 (Canada); Department of Materials and Metallurgical Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Arabi, H.; Mirdamadi, S.; Ghorbani, H. [Department of Materials and Metallurgical Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Coyle, T.W. [Centre for Advanced Coating Technologies, Department of Materials Science and Engineering, University of Toronto, 184 College Street, Toronto, Ontario M5S 3E4 (Canada)

2011-09-15

Metal dusting attack has been examined after three 168 h cycles on two Ni-50Cr coatings with different microstructures deposited on 310S alloy substrates by the high velocity oxy-fuel (HVOF) thermal-spray process. Metal dusting in uncoated 310S alloy specimens was found to be still in the initiation stage after 504 h of exposure in the 50H{sub 2}:50CO gas environment at 620 C. Dense Ni-50Cr coatings offered suitable resistance to metal dusting. Metal dusting was observed in the 310S substrates adjacent to pores at the interface between the substrate and a porous Ni-50Cr coating. The porosity present in the as-deposited coatings was shown to introduce a large variability into coating performance. Carbon formed by decomposition of the gaseous species accumulated in the surface pores and resulted in the dislodgement of surface splats due to stresses generated by the volume changes. When the corrosive gas atmosphere was able to penetrate through the interconnected pores and reach the coating-substrate interface, the 310S substrate was carburized, metal dusting attack occurred, and the resulting formation of coke in the pores led to local failure of the coating. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Small-signal model for the series resonant converter

Science.gov (United States)

King, R. J.; Stuart, T. A.

1985-01-01

The results of a previous discrete-time model of the series resonant dc-dc converter are reviewed and from these a small signal dynamic model is derived. This model is valid for low frequencies and is based on the modulation of the diode conduction angle for control. The basic converter is modeled separately from its output filter to facilitate the use of these results for design purposes. Experimental results are presented.

41 CFR 101-8.310 - New construction.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 2 2010-07-01 2010-07-01 true New construction. 101-8... FINANCIAL ASSISTANCE 8.3-Discrimination Prohibited on the Basis of Handicap § 101-8.310 New construction. (a) Design and construction. Each facility or part of a facility constructed by, on behalf of, or for the use...

Recursive Bayesian recurrent neural networks for time-series modeling.

Science.gov (United States)

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect

Directory of Open Access Journals (Sweden)

Yanhui Xi

2016-01-01

Full Text Available The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation. With the new representations, a theoretical explanation of leverage effect is provided. Simulated data and daily stock market indices (Shanghai composite index, Shenzhen component index, and Standard and Poor’s 500 Composite index via Bayesian Markov Chain Monte Carlo (MCMC method are used to estimate the leverage market microstructure model. The results verify the effectiveness of the model and its estimation approach proposed in the paper and also indicate that the stock markets have strong leverage effects. Compared with the classical leverage stochastic volatility (SV model in terms of DIC (Deviance Information Criterion, the leverage market microstructure model fits the data better.

Diffusion coefficients of nickel chloride in aqueous solutions of lactose at T = 298.15 K and T = 310.15 K

Energy Technology Data Exchange (ETDEWEB)

Ribeiro, Ana C.F., E-mail: anacfrib@ci.uc.p [Department of Chemistry, University of Coimbra, 3004-535 Coimbra (Portugal); Gomes, Joselaine C.S., E-mail: leidygomes18@hotmail.co [Department of Chemistry, University of Coimbra, 3004-535 Coimbra (Portugal); Barros, Marisa C.F., E-mail: marisa.barros@gmail.co [Department of Chemistry, University of Coimbra, 3004-535 Coimbra (Portugal); Lobo, Victor M.M., E-mail: vlobo@ci.uc.p [Department of Chemistry, University of Coimbra, 3004-535 Coimbra (Portugal); Esteso, Miguel A., E-mail: miguel.esteso@uah.e [Departamento de Quimica Fisica, Facultad de Farmacia, Universidad de Alcala, 28871, Alcala de Henares (Madrid) (Spain)

2011-03-15

Binary mutual diffusion coefficients (interdiffusion coefficients) of nickel chloride in water at T = 298.15 K and T = 310.15 K, and at concentrations between (0.000 and 0.100) mol {center_dot} dm{sup -3}, using a Taylor dispersion method have been measured. These data are discussed on the basis of the Onsager-Fuoss and Pikal models. The equivalent conductance at infinitesimal concentration of the nickel ion in these solutions at T = 310.15 K has been estimated using these results. Through the same technique, ternary mutual diffusion coefficients (D{sub 11}, D{sub 22}, D{sub 12}, and D{sub 21}) for aqueous solutions containing NiCl{sub 2} and lactose, at T = 298.15 K and T = 310.15 K, and at different carrier concentrations were also measured. These data permit us to have a better understanding of the structure of these systems and the thermodynamic behaviour of NiCl{sub 2} in different media.

Time series regression model for infectious disease and weather.

Science.gov (United States)

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

PENDISC: a simple method for constructing a mathematical model from time-series data of metabolite concentrations.

Science.gov (United States)

Sriyudthsak, Kansuporn; Iwata, Michio; Hirai, Masami Yokota; Shiraishi, Fumihide

2014-06-01

The availability of large-scale datasets has led to more effort being made to understand characteristics of metabolic reaction networks. However, because the large-scale data are semi-quantitative, and may contain biological variations and/or analytical errors, it remains a challenge to construct a mathematical model with precise parameters using only these data. The present work proposes a simple method, referred to as PENDISC (Parameter Estimation in a N on- DImensionalized S-system with Constraints), to assist the complex process of parameter estimation in the construction of a mathematical model for a given metabolic reaction system. The PENDISC method was evaluated using two simple mathematical models: a linear metabolic pathway model with inhibition and a branched metabolic pathway model with inhibition and activation. The results indicate that a smaller number of data points and rate constant parameters enhances the agreement between calculated values and time-series data of metabolite concentrations, and leads to faster convergence when the same initial estimates are used for the fitting. This method is also shown to be applicable to noisy time-series data and to unmeasurable metabolite concentrations in a network, and to have a potential to handle metabolome data of a relatively large-scale metabolic reaction system. Furthermore, it was applied to aspartate-derived amino acid biosynthesis in Arabidopsis thaliana plant. The result provides confirmation that the mathematical model constructed satisfactorily agrees with the time-series datasets of seven metabolite concentrations.

Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

Science.gov (United States)

Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

2005-12-01

Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

Time Series Modelling using Proc Varmax

DEFF Research Database (Denmark)

Milhøj, Anders

2007-01-01

In this paper it will be demonstrated how various time series problems could be met using Proc Varmax. The procedure is rather new and hence new features like cointegration, testing for Granger causality are included, but it also means that more traditional ARIMA modelling as outlined by Box...

33 CFR 149.310 - What are the muster and embarkation requirements for survival craft?

Science.gov (United States)

2010-07-01

... embarkation requirements for survival craft? 149.310 Section 149.310 Navigation and Navigable Waters COAST... and embarkation requirements for survival craft? Muster and embarkation arrangements for survival craft must comply with 46 CFR 108.540. ...

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

Science.gov (United States)

Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

2009-08-01

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

32 CFR 310.33 - New and altered record systems.

Science.gov (United States)

2010-07-01

... such as tape devices, disk devices, card readers, printers, and similar devices to an existing IT... master registry contains a current system notice for the system. (see § 310.32(q)). (2) The DPO...

Modeling Periodic Impulsive Effects on Online TV Series Diffusion.

Science.gov (United States)

Fu, Peihua; Zhu, Anding; Fang, Qiwen; Wang, Xi

Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities

Modeling Periodic Impulsive Effects on Online TV Series Diffusion.

Directory of Open Access Journals (Sweden)

Peihua Fu

Full Text Available Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data.We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution.We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation.To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public

Modeling Periodic Impulsive Effects on Online TV Series Diffusion

Science.gov (United States)

Fang, Qiwen; Wang, Xi

2016-01-01

Background Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. Methods We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. Results We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. Conclusion To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount

Modeling financial time series with S-plus

CERN Document Server

Zivot, Eric

2003-01-01

The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...

20 CFR 669.310 - What are the basic components of an NFJP service delivery strategy?

Science.gov (United States)

2010-04-01

... include: (a) A customer-centered case management approach; (b) The provision of workforce investment... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false What are the basic components of an NFJP service delivery strategy? 669.310 Section 669.310 Employees' Benefits EMPLOYMENT AND TRAINING...

Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

Md. Rabiul Islam

2012-01-01

Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

42 CFR 421.310 - Conflict of interest requirements.

Science.gov (United States)

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Conflict of interest requirements. 421.310 Section... Conflict of interest requirements. Offerors for MIP contracts and MIP contractors are subject to the following: (a) The conflict of interest standards and requirements of the Federal Acquisition Regulation...

High-temperature series expansions for random Potts models

Directory of Open Access Journals (Sweden)

M.Hellmund

2005-01-01

Full Text Available We discuss recently generated high-temperature series expansions for the free energy and the susceptibility of random-bond q-state Potts models on hypercubic lattices. Using the star-graph expansion technique, quenched disorder averages can be calculated exactly for arbitrary uncorrelated coupling distributions while keeping the disorder strength p as well as the dimension d as symbolic parameters. We present analyses of the new series for the susceptibility of the Ising (q=2 and 4-state Potts model in three dimensions up to the order 19 and 18, respectively, and compare our findings with results from field-theoretical renormalization group studies and Monte Carlo simulations.

Neural network versus classical time series forecasting models

Science.gov (United States)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

A multivariate time series approach to modeling and forecasting demand in the emergency department.

Science.gov (United States)

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Identification of neutral biochemical network models from time series data.

Science.gov (United States)

Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S

2009-05-05

The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

TIME SERIES MODELS OF THREE SETS OF RXTE OBSERVATIONS OF 4U 1543–47

International Nuclear Information System (INIS)

Koen, C.

2013-01-01

The X-ray nova 4U 1543–47 was in a different physical state (low/hard, high/soft, and very high) during the acquisition of each of the three time series analyzed in this paper. Standard time series models of the autoregressive moving average (ARMA) family are fitted to these series. The low/hard data can be adequately modeled by a simple low-order model with fixed coefficients, once the slowly varying mean count rate has been accounted for. The high/soft series requires a higher order model, or an ARMA model with variable coefficients. The very high state is characterized by a succession of 'dips', with roughly equal depths. These seem to appear independently of one another. The underlying stochastic series can again be modeled by an ARMA form, or roughly as the sum of an ARMA series and white noise. The structuring of each model in terms of short-lived aperiodic and 'quasi-periodic' components is discussed.

a Landsat Time-Series Stacks Model for Detection of Cropland Change

Science.gov (United States)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

31 CFR 594.310 - Specially designated global terrorist; SDGT.

Science.gov (United States)

2010-07-01

... (Continued) OFFICE OF FOREIGN ASSETS CONTROL, DEPARTMENT OF THE TREASURY GLOBAL TERRORISM SANCTIONS REGULATIONS General Definitions § 594.310 Specially designated global terrorist; SDGT. The term specially...

Quality Quandaries- Time Series Model Selection and Parsimony

DEFF Research Database (Denmark)

Bisgaard, Søren; Kulahci, Murat

2009-01-01

Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....

15 CFR 310.4 - Action on application.

Science.gov (United States)

2010-01-01

... applications by the Director, complete with all exhibits required by § 310.3. (2) The financial plans of the... understanding of the issues which require clarification. The Director or Examiner shall impress upon the parties... that might have been raised by the application. (f) Statements of interested parties may be presented...

13 CFR 108.310 - Contents of application.

Science.gov (United States)

2010-01-01

... Section 108.310 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION NEW MARKETS VENTURE CAPITAL... Applicant must indicate— (1) The specific amount of Regulatory Capital it proposes to raise (which amount... of which must be not less than $1,500,000 or 30 percent of the Regulatory Capital it proposes to...

A dynamic model to explain hydration behaviour along the lanthanide series

International Nuclear Information System (INIS)

Duvail, M.; Spezia, R.; Vitorge, P.

2008-01-01

An understanding of the hydration structure of heavy atoms, such as transition metals, lanthanides and actinides, in aqueous solution is of fundamental importance in order to address their solvation properties and chemical reactivity. Herein we present a systematic molecular dynamics study of Ln 3+ hydration in bulk water that can be used as reference for experimental and theoretical research in this and related fields. Our study of hydration structure and dynamics along the entire Ln 3+ series provides a dynamic picture of the CN behavioural change from light (CN=9 predominating) to heavy (CN=8 predominating) lanthanides consistent with the exchange mechanism proposed by Helm, Merbach and co-workers. This scenario is summarized in this work. The hydrated light lanthanides are stable TTP structures containing two kinds of water molecules: six molecules forming the trigonal prism and three in the centre triangle. Towards the middle of the series both ionic radii and polarizabilities decrease, such that first-shell water-water repulsion increases and water-cation attraction decreases. This mainly applies for molecules of the centre triangle of the nine-fold structure. Thus, one of these molecules stay in the second hydration sphere of the lanthanide for longer average times, as one progresses along the lanthanide series. The interchange between predominantly CN=9 and CN=8 is found between Tb and Dy. Therefore, we propose a model that determines the properties governing the change in the first-shell coordination number across the series, confirming the basic hypothesis proposed by Helm and Merbach. We show that it is not a sudden change in behaviour, but rather that it results from a statistical predominance of one first hydration shell structure containing nine water molecules over one containing eight. This is observed progressively across the series. (O.M.)

New series of 3 D lattice integrable models

International Nuclear Information System (INIS)

Mangazeev, V.V.; Sergeev, S.M.; Stroganov, Yu.G.

1993-01-01

In this paper we present a new series of 3-dimensional integrable lattice models with N colors. The weight functions of the models satisfy modified tetrahedron equations with N states and give a commuting family of two-layer transfer-matrices. The dependence on the spectral parameters corresponds to the static limit of the modified tetrahedron equations and weights are parameterized in terms of elliptic functions. The models contain two free parameters: elliptic modulus and additional parameter η. 12 refs

Single-Index Additive Vector Autoregressive Time Series Models

KAUST Repository

LI, YEHUA; GENTON, MARC G.

2009-01-01

We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided

Forecasting daily meteorological time series using ARIMA and regression models

Science.gov (United States)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

28 CFR 36.310 - Transportation provided by public accommodations.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Transportation provided by public... BASIS OF DISABILITY BY PUBLIC ACCOMMODATIONS AND IN COMMERCIAL FACILITIES Specific Requirements § 36.310 Transportation provided by public accommodations. (a) General. (1) A public accommodation that provides...

Identification of neutral biochemical network models from time series data

Directory of Open Access Journals (Sweden)

Maia Marco

2009-05-01

Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

Bayesian dynamic modeling of time series of dengue disease case counts.

Science.gov (United States)

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful

Road safety forecasts in five European countries using structural time series models.

Science.gov (United States)

Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George

2014-01-01

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

OpenAIRE

Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...

Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

Science.gov (United States)

Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

2017-09-01

Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

Automated Bayesian model development for frequency detection in biological time series

Directory of Open Access Journals (Sweden)

Oldroyd Giles ED

2011-06-01

Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

Automated Bayesian model development for frequency detection in biological time series.

Science.gov (United States)

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time

Tempered fractional time series model for turbulence in geophysical flows

Science.gov (United States)

Meerschaert, Mark M.; Sabzikar, Farzad; Phanikumar, Mantha S.; Zeleke, Aklilu

2014-09-01

We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model.

Time series modeling for syndromic surveillance

Directory of Open Access Journals (Sweden)

Mandl Kenneth D

2003-01-01

Full Text Available Abstract Background Emergency department (ED based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool

Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model

DEFF Research Database (Denmark)

Boke-Olen, Niklas; Lehsten, Veiko; Ardo, Jonas

2016-01-01

cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses...... climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create...... a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore...

Creation and evaluation of a database of renewable production time series and other data for energy system modelling

International Nuclear Information System (INIS)

Janker, Karl Albert

2015-01-01

This thesis describes a model which generates renewable power generation time series as input data for energy system models. The focus is on photovoltaic systems and wind turbines. The basis is a high resolution global raster data set of weather data for many years. This data is validated, corrected and preprocessed. The composition of the hourly generation data is done via simulation of the respective technology. The generated time series are aggregated for different regions and are validated against historical production time series.

Tempered fractional time series model for turbulence in geophysical flows

International Nuclear Information System (INIS)

Meerschaert, Mark M; Sabzikar, Farzad; Phanikumar, Mantha S; Zeleke, Aklilu

2014-01-01

We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model. (paper)

New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

Science.gov (United States)

Song, Qiang; Chissom, Brad S.

Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…

47 CFR 80.310 - Watch required by voluntary vessels.

Science.gov (United States)

2010-10-01

... Section 80.310 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) SAFETY AND SPECIAL RADIO SERVICES STATIONS IN THE MARITIME SERVICES Safety Watch Requirements and Procedures Ship Station Safety...] Distress, Alarm, Urgency and Safety Procedures ...

46 CFR 310.9 - Medical attention and injury claims.

Science.gov (United States)

2010-10-01

... Regulations and Minimum Standards for State, Territorial or Regional Maritime Academies and Colleges § 310.9... employees shall look to the State alone for pay, allowances, compensation and other benefits during injury...

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

Science.gov (United States)

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Assessing and improving the quality of modeling : a series of empirical studies about the UML

NARCIS (Netherlands)

Lange, C.F.J.

2007-01-01

Assessing and Improving the Quality of Modeling A Series of Empirical Studies about the UML This thesis addresses the assessment and improvement of the quality of modeling in software engineering. In particular, we focus on the Unified Modeling Language (UML), which is the de facto standard in

A novel model for Time-Series Data Clustering Based on piecewise SVD and BIRCH for Stock Data Analysis on Hadoop Platform

Directory of Open Access Journals (Sweden)

Ibgtc Bowala

2017-06-01

Full Text Available With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for forecasting time series data, but accurate clusters are a pre-requirement. Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes. However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level. A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach. Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering. The algorithm is a novel model that can handle massive time series data. Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.

On the maximum-entropy/autoregressive modeling of time series

Science.gov (United States)

Chao, B. F.

1984-01-01

The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.

Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

Directory of Open Access Journals (Sweden)

Stepčenko Artūrs

2016-12-01

Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.

Nonlinear detection of disordered voice productions from short time series based on a Volterra-Wiener-Korenberg model

Energy Technology Data Exchange (ETDEWEB)

Zhang Yu, E-mail: yuzhang@xmu.edu.cn [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Sprecher, Alicia J. [Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States); Zhao Zongxi [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Jiang, Jack J. [Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States)

2011-09-15

Highlights: > The VWK method effectively detects the nonlinearity of a discrete map. > The method describes the chaotic time series of a biomechanical vocal fold model. > Nonlinearity in laryngeal pathology is detected from short and noisy time series. - Abstract: In this paper, we apply the Volterra-Wiener-Korenberg (VWK) model method to detect nonlinearity in disordered voice productions. The VWK method effectively describes the nonlinearity of a third-order nonlinear map. It allows for the analysis of short and noisy data sets. The extracted VWK model parameters show an agreement with the original nonlinear map parameters. Furthermore, the VWK mode method is applied to successfully assess the nonlinearity of a biomechanical voice production model simulating irregular vibratory dynamics of vocal folds with a unilateral vocal polyp. Finally, we show the clinical applicability of this nonlinear detection method to analyze the electroglottographic data generated by 14 patients with vocal nodules or polyps. The VWK model method shows potential in describing the nonlinearity inherent in disordered voice productions from short and noisy time series that are common in the clinical setting.

Nonlinear detection of disordered voice productions from short time series based on a Volterra-Wiener-Korenberg model

International Nuclear Information System (INIS)

Zhang Yu; Sprecher, Alicia J.; Zhao Zongxi; Jiang, Jack J.

2011-01-01

Highlights: → The VWK method effectively detects the nonlinearity of a discrete map. → The method describes the chaotic time series of a biomechanical vocal fold model. → Nonlinearity in laryngeal pathology is detected from short and noisy time series. - Abstract: In this paper, we apply the Volterra-Wiener-Korenberg (VWK) model method to detect nonlinearity in disordered voice productions. The VWK method effectively describes the nonlinearity of a third-order nonlinear map. It allows for the analysis of short and noisy data sets. The extracted VWK model parameters show an agreement with the original nonlinear map parameters. Furthermore, the VWK mode method is applied to successfully assess the nonlinearity of a biomechanical voice production model simulating irregular vibratory dynamics of vocal folds with a unilateral vocal polyp. Finally, we show the clinical applicability of this nonlinear detection method to analyze the electroglottographic data generated by 14 patients with vocal nodules or polyps. The VWK model method shows potential in describing the nonlinearity inherent in disordered voice productions from short and noisy time series that are common in the clinical setting.

9 CFR 310.5 - Condemned carcasses and parts to be so marked; tanking; separation.

Science.gov (United States)

2010-01-01

... 9 Animals and Animal Products 2 2010-01-01 2010-01-01 false Condemned carcasses and parts to be so marked; tanking; separation. 310.5 Section 310.5 Animals and Animal Products FOOD SAFETY AND INSPECTION... marked shall be placed immediately in trucks or receptacles which shall be kept plainly marked “U.S...

Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

Directory of Open Access Journals (Sweden)

H. Sadeghi

2016-02-01

Full Text Available Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety of water demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand in better planning of water resources in arid and semiarid regions are faced with water restrictions. Materials and Methods: One of the most important factors affecting the changing technological advances in production and demand functions, we must pay special attention to the layout pattern. Technology development is concerned not only technically, but also other aspects such as personal, non-economic factors (population, geographical and social factors can be analyzed. Model examined in this study, a regression model is composed of a series of structural components over time allows changed invisible accidentally. Explanatory variables technology (both crystalline and amorphous in a model according to which the material is said to be better, but because of the lack of measured variables over time can not be entered in the template. Model examined in this study, a regression model is composed of a series of structural component invisible accidentally changed over time allows. In this study, structural time series (STSM and ARMA time series models have been used to model and estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models have been compared to each other. The desired data in this research include water consumption in Isfahan, water price and the monthly pay

Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

Science.gov (United States)

Miranian, A; Abdollahzade, M

2013-02-01

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

Assimilation of LAI time-series in crop production models

Science.gov (United States)

Kooistra, Lammert; Rijk, Bert; Nannes, Louis

2014-05-01

Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor

32 CFR 310.50 - Lost, stolen, or compromised information.

Science.gov (United States)

2010-07-01

... Official for Privacy within 24 hours of discovering that a breach of personally identifiable information... Privacy Office of the breach within 48 hours upon being notified that a loss, theft, or compromise has... (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Privacy Act Violations § 310.50 Lost, stolen, or compromised...

ShapeSelectForest: a new r package for modeling landsat time series

Science.gov (United States)

Mary Meyer; Xiyue Liao; Gretchen Moisen; Elizabeth Freeman

2015-01-01

We present a new R package called ShapeSelectForest recently posted to the Comprehensive R Archival Network. The package was developed to fit nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral...

43 CFR 46.310 - Contents of an environmental assessment.

Science.gov (United States)

2010-10-01

... implementation without the need for further analysis. Adaptive management includes a monitoring component... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Contents of an environmental assessment... OF THE NATIONAL ENVIRONMENTAL POLICY ACT OF 1969 Environmental Assessments § 46.310 Contents of an...

The application of time series models to cloud field morphology analysis

Science.gov (United States)

Chin, Roland T.; Jau, Jack Y. C.; Weinman, James A.

1987-01-01

A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.

78 FR 75511 - Special Conditions: Bombardier Inc., Models BD-500-1A10 and BD-500-1A11 Series Airplanes...

Science.gov (United States)

2013-12-12

... Inc., Models BD-500-1A10 and BD- 500-1A11 Series Airplanes; Electronic Flight Control System: Control... Inc. Models BD-500-1A10 and BD-500-1A11 series airplanes. These airplanes will have a novel or unusual... comments, data, or views. The most helpful comments reference a specific portion of the special conditions...

Evaluation and Control of Mechanical Degradation of Austenitic Stainless 310S Steel Substrate During Coated Superconductor Processing

Science.gov (United States)

Kim, Seung-Gyu; Kim, Najung; Shim, Hyung-Seok; Kwon, Oh Min; Kwon, Dongil

2018-05-01

The superconductor industry considers cold-rolled austenitic stainless 310S steel a less expensive substitute for Hastelloy X as a substrate for coated superconductor. However, the mechanical properties of cold-rolled 310S substrate degrade significantly in the superconductor deposition process. To overcome this, we applied hot rolling at 900 °C (or 1000 °C) to the 310S substrate. To check the property changes, a simulated annealing condition equivalent to that used in manufacturing was determined and applied. The effects of the hot rolling on the substrate were evaluated by analyzing its physical properties and texture.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Directory of Open Access Journals (Sweden)

Jun-He Yang

2017-01-01

Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model

Science.gov (United States)

Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.

2009-04-01

The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.

40 CFR 91.310 - Engine intake air humidity measurement.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 20 2010-07-01 2010-07-01 false Engine intake air humidity measurement... Provisions § 91.310 Engine intake air humidity measurement. This section refers to engines which are supplied... air, the ambient testcell humidity measurement may be used. (a) Humidity conditioned air supply. Air...

40 CFR 90.310 - Engine intake air humidity measurement.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 20 2010-07-01 2010-07-01 false Engine intake air humidity measurement... Emission Test Equipment Provisions § 90.310 Engine intake air humidity measurement. This section refers to... for the engine intake air, the ambient test cell humidity measurement may be used. (a) Humidity...

Single event time series analysis in a binary karst catchment evaluated using a groundwater model (Lurbach system, Austria).

Science.gov (United States)

Mayaud, C; Wagner, T; Benischke, R; Birk, S

2014-04-16

The Lurbach karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massif itself and a sinking stream that originates in low permeable schists (allogenic recharge). Detailed data from two events recorded during a tracer experiment in 2008 demonstrate that an overflow from one of the sub-catchments to the other is activated if the discharge of the main spring exceeds a certain threshold. Time series analysis (autocorrelation and cross-correlation) was applied to examine to what extent the various available methods support the identification of the transient inter-catchment flow observed in this binary karst system. As inter-catchment flow is found to be intermittent, the evaluation was focused on single events. In order to support the interpretation of the results from the time series analysis a simplified groundwater flow model was built using MODFLOW. The groundwater model is based on the current conceptual understanding of the karst system and represents a synthetic karst aquifer for which the same methods were applied. Using the wetting capability package of MODFLOW, the model simulated an overflow similar to what has been observed during the tracer experiment. Various intensities of allogenic recharge were employed to generate synthetic discharge data for the time series analysis. In addition, geometric and hydraulic properties of the karst system were varied in several model scenarios. This approach helps to identify effects of allogenic recharge and aquifer properties in the results from the time series analysis. Comparing the results from the time series analysis of the observed data with those of the synthetic data a good agreement was found. For instance, the cross-correlograms show similar patterns with respect to time lags and maximum cross-correlation coefficients if appropriate hydraulic parameters are assigned to the groundwater model. The comparable behaviors of the real and the

Bayesian near-boundary analysis in basic macroeconomic time series models

NARCIS (Netherlands)

M.D. de Pooter (Michiel); F. Ravazzolo (Francesco); R. Segers (René); H.K. van Dijk (Herman)

2008-01-01

textabstractSeveral lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

Science.gov (United States)

Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

A robust interrupted time series model for analyzing complex health care intervention data

KAUST Repository

Cruz, Maricela

2017-08-29

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be

A robust interrupted time series model for analyzing complex health care intervention data

KAUST Repository

Cruz, Maricela; Bender, Miriam; Ombao, Hernando

2017-01-01

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be

21 CFR 111.310 - What are the requirements for the laboratory facilities that you use?

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 2 2010-04-01 2010-04-01 false What are the requirements for the laboratory facilities that you use? 111.310 Section 111.310 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION CURRENT GOOD MANUFACTURING PRACTICE IN...

Development of Simulink-Based SiC MOSFET Modeling Platform for Series Connected Devices

DEFF Research Database (Denmark)

Tsolaridis, Georgios; Ilves, Kalle; Reigosa, Paula Diaz

2016-01-01

A new MATLAB/Simulink-based modeling platform has been developed for SiC MOSFET power modules. The modeling platform describes the electrical behavior f a single 1.2 kV/ 350 A SiC MOSFET power module, as well as the series connection of two of them. A fast parameter initialization is followed...... by an optimization process to facilitate the extraction of the model’s parameters in a more automated way relying on a small number of experimental waveforms. Through extensive experimental work, it is shown that the model accurately predicts both static and dynamic performances. The series connection of two Si......C power modules has been investigated through the validation of the static and dynamic conditions. Thanks to the developed model, a better understanding of the challenges introduced by uneven voltage balance sharing among series connected devices is possible....

40 CFR 310.15 - How do I apply for reimbursement?

Science.gov (United States)

2010-07-01

... RESPONSE TO HAZARDOUS SUBSTANCE RELEASES Provisions How to Get Reimbursed § 310.15 How do I apply for... Management, Office of Solid Waste and Emergency Response, Environmental Protection Agency, 1200 Pennsylvania...

Modeling the full-bridge series-resonant power converter

Science.gov (United States)

King, R. J.; Stuart, T. A.

1982-01-01

A steady state model is derived for the full-bridge series-resonant power converter. Normalized parametric curves for various currents and voltages are then plotted versus the triggering angle of the switching devices. The calculations are compared with experimental measurements made on a 50 kHz converter and a discussion of certain operating problems is presented.

Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

Science.gov (United States)

Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.

2015-06-01

This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.

Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models

DEFF Research Database (Denmark)

Hillebrand, Eric Tobias; Medeiros, Marcelo C.

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building...

Time-series models on somatic cell score improve detection of matistis

DEFF Research Database (Denmark)

Norberg, E; Korsgaard, I R; Sloth, K H M N

2008-01-01

In-line detection of mastitis using frequent milk sampling was studied in 241 cows in a Danish research herd. Somatic cell scores obtained at a daily basis were analyzed using a mixture of four time-series models. Probabilities were assigned to each model for the observations to belong to a normal...... "steady-state" development, change in "level", change of "slope" or "outlier". Mastitis was indicated from the sum of probabilities for the "level" and "slope" models. Time-series models were based on the Kalman filter. Reference data was obtained from veterinary assessment of health status combined...... with bacteriological findings. At a sensitivity of 90% the corresponding specificity was 68%, which increased to 83% using a one-step back smoothing. It is concluded that mixture models based on Kalman filters are efficient in handling in-line sensor data for detection of mastitis and may be useful for similar...

Time-series modeling of long-term weight self-monitoring data.

Science.gov (United States)

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

Science.gov (United States)

Houseman, E Andres; Virji, M Abbas

2017-08-01

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates

Crystallization and molecular-replacement studies of the monoclonal antibody mAbR310 specific for the (R)-HNE-modified protein

International Nuclear Information System (INIS)

Ito, Sohei; Tatsuda, Emi; Ishino, Kousuke; Suzuki, Kenichiro; Sakai, Hiroshi; Uchida, Koji

2006-01-01

Antigen-free Fab fragment of mAbR310, which recognizes (R)-HNE modified protein, has been crystallized. Initial phases have been obtained by molecular replacement. 4-Hydroxy-2-nonenal (HNE), a major racemic product of lipid peroxidation, reacts with histidine to form a stable HNE–histidine Michael addition-type adduct possessing three chiral centres in the cyclic hemiacetal structure. Monoclonal antibodies against HNE-modified protein have been widely used for assessing oxidative stress in vitro and in vivo. Here, the purification, crystallization and preliminary crystallographic analysis of a Fab fragment of novel monoclonal antibody R310 (mAbR310), which recognizes (R)-HNE-modified protein, are reported. The Fab fragment of mAbR310 was obtained by digestion with papain, purified and crystallized. Using hanging-drop vapour-diffusion crystallization techniques, crystals of mAbR310 Fab were obtained. The crystal belongs to the monoclinic space group C2 (unit-cell parameters a = 127.04, b = 65.31, c = 64.29 Å, β = 118.88°) and diffracted X-rays to a resolution of 1.84 Å. The asymmetric unit contains one molecule of mAbR310, with a corresponding crystal volume per protein weight of 2.51 Å 3 Da −1 and a solvent content of 51.0%

From Taylor series to Taylor models

International Nuclear Information System (INIS)

Berz, Martin

1997-01-01

An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems

30 CFR 75.310 - Installation of main mine fans.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Installation of main mine fans. 75.310 Section... mine fans. (a) Each main mine fan shall be— (1) Installed on the surface in an incombustible housing... that gives a signal at the mine when the fan either slows or stops. A responsible person designated by...

Disease management with ARIMA model in time series.

Science.gov (United States)

Sato, Renato Cesar

2013-01-01

The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.

A new Markov-chain-related statistical approach for modelling synthetic wind power time series

International Nuclear Information System (INIS)

Pesch, T; Hake, J F; Schröders, S; Allelein, H J

2015-01-01

The integration of rising shares of volatile wind power in the generation mix is a major challenge for the future energy system. To address the uncertainties involved in wind power generation, models analysing and simulating the stochastic nature of this energy source are becoming increasingly important. One statistical approach that has been frequently used in the literature is the Markov chain approach. Recently, the method was identified as being of limited use for generating wind time series with time steps shorter than 15–40 min as it is not capable of reproducing the autocorrelation characteristics accurately. This paper presents a new Markov-chain-related statistical approach that is capable of solving this problem by introducing a variable second lag. Furthermore, additional features are presented that allow for the further adjustment of the generated synthetic time series. The influences of the model parameter settings are examined by meaningful parameter variations. The suitability of the approach is demonstrated by an application analysis with the example of the wind feed-in in Germany. It shows that—in contrast to conventional Markov chain approaches—the generated synthetic time series do not systematically underestimate the required storage capacity to balance wind power fluctuation. (paper)

77 FR 64701 - Airworthiness Directives; Airbus Airplanes

Science.gov (United States)

2012-10-23

... Airworthiness Directives; Airbus Airplanes AGENCY: Federal Aviation Administration (FAA), Department of... Airbus Model A300 series airplanes; Model A310 series airplanes; and Model A300 B4-600, B4-600R, and F4... that the AD be effective after Airbus completes certifying the improved design for the fuel pump half...

40 CFR 310.4 - What abbreviations should I know?

Science.gov (United States)

2010-07-01

... RESPONSE TO HAZARDOUS SUBSTANCE RELEASES General Information § 310.4 What abbreviations should I know? The.... 11000-11050). LEPC—Local Emergency Planning Committee. NCP—National Oil and Hazardous Substances... Response Center. OMB—Office of Management and Budget. PRP—Potentially Responsible Party. SARA—The Superfund...

A perturbative approach for enhancing the performance of time series forecasting.

Science.gov (United States)

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Clustering Multivariate Time Series Using Hidden Markov Models

Directory of Open Access Journals (Sweden)

Shima Ghassempour

2014-03-01

Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

45 CFR 310.15 - What are the safeguards and processes that comprehensive Tribal IV-D agencies must have in place...

Science.gov (United States)

2010-10-01

... 45 Public Welfare 2 2010-10-01 2010-10-01 false What are the safeguards and processes that... IV-D Systems and Office Automation? 310.15 Section 310.15 Public Welfare Regulations Relating to... AND OFFICE AUTOMATION Requirements for Computerized Tribal IV-D Systems and Office Automation § 310.15...

On determining the prediction limits of mathematical models for time series

International Nuclear Information System (INIS)

Peluso, E.; Gelfusa, M.; Lungaroni, M.; Talebzadeh, S.; Gaudio, P.; Murari, A.; Contributors, JET

2016-01-01

Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.

Modeling multivariate time series on manifolds with skew radial basis functions.

Science.gov (United States)

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm.

Science.gov (United States)

Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Cao, Jie; Liu, Xiaohui

2013-12-01

In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.

Single-Index Additive Vector Autoregressive Time Series Models

KAUST Repository

LI, YEHUA

2009-09-01

We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.

Clinical time series prediction: towards a hierarchical dynamical system framework

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models

Directory of Open Access Journals (Sweden)

Bao Sheng Loe

2018-04-01

Full Text Available This study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG. The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study. The 16-item ICAR (International Cognitive Ability Resource1 short form ability test was used to evaluate construct validity. The Rasch Model and two Linear Logistic Test Model(s (LLTM were employed to estimate and predict the item parameters. Results indicate that a single factor determines the performance on tests composed of items generated by the ANSIG. Under the LLTM approach, all the cognitive operators were significant predictors of item difficulty. Moderate to high correlations were evident between the number series items and the ICAR test scores, with high correlation found for the ICAR Letter-Numeric-Series type items, suggesting adequate nomothetic span. Extended cognitive research is, nevertheless, essential for the automatic generation of an item pool with predictable psychometric properties.

Incorporating Satellite Time-Series Data into Modeling

Science.gov (United States)

Gregg, Watson

2008-01-01

In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

Empirical intrinsic geometry for nonlinear modeling and time series filtering.

Science.gov (United States)

Talmon, Ronen; Coifman, Ronald R

2013-07-30

In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.

340 and 310 drawing field verification

International Nuclear Information System (INIS)

Langdon, J.

1996-01-01

The purpose of the drawing field verification work plan is to provide reliable drawings for the 310 Treated Effluent Disposal Facility (TEDF) and 340 Waste Handling Facility (340 Facility). The initial scope of this work plan is to provide field verified and updated versions of all the 340 Facility essential drawings. This plan can also be used for field verification of any other drawings that the facility management directs to be so updated. Any drawings revised by this work plan will be issued in an AutoCAD format

Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization

Science.gov (United States)

Westenbroek, Stephen M.; Doherty, John; Walker, John F.; Kelson, Victor A.; Hunt, Randall J.; Cera, Timothy B.

2012-01-01

The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST (Parameter ESTimation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.

Transfer function modeling of the monthly accumulated rainfall series over the Iberian Peninsula

Energy Technology Data Exchange (ETDEWEB)

Mateos, Vidal L.; Garcia, Jose A.; Serrano, Antonio; De la Cruz Gallego, Maria [Departamento de Fisica, Universidad de Extremadura, Badajoz (Spain)

2002-10-01

In order to improve the results given by Autoregressive Moving-Average (ARMA) modeling for the monthly accumulated rainfall series taken at 19 observatories of the Iberian Peninsula, a Discrete Linear Transfer Function Noise (DLTFN) model was applied taking the local pressure series (LP), North Atlantic sea level pressure series (SLP) and North Atlantic sea surface temperature (SST) as input variables, and the rainfall series as the output series. In all cases, the performance of the DLTFN models, measured by the explained variance of the rainfall series, is better than the performance given by the ARMA modeling. The best performance is given by the models that take the local pressure as the input variable, followed by the sea level pressure models and the sea surface temperature models. Geographically speaking, the models fitted to those observatories located in the west of the Iberian Peninsula work better than those on the north and east of the Peninsula. Also, it was found that there is a region located between 0 N and 20 N, which shows the highest cross-correlation between SST and the peninsula rainfalls. This region moves to the west and northwest off the Peninsula when the SLP series are used. [Spanish] Con el objeto de mejorar los resultados porporcionados por los modelos Autorregresivo Media Movil (ARMA) ajustados a las precipitaciones mensuales acumuladas registradas en 19 observatorios de la Peninsula Iberica se han usado modelos de funcion de transferencia (DLTFN) en los que se han empleado como variable independiente la presion local (LP), la presion a nivel del mar (SLP) o la temperatura de agua del mar (SST) en el Atlantico Norte. En todos los casos analizados, los resultados obtenidos con los modelos DLTFN, medidos mediante la varianza explicada por el modelo, han sido mejores que los resultados proporcionados por los modelos ARMA. Los mejores resultados han sido dados por aquellos modelos que usan la presion local como variable de entrada, seguidos

From Taylor series to Taylor models

International Nuclear Information System (INIS)

Berz, M.

1997-01-01

An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems. copyright 1997 American Institute of Physics

Clinical time series prediction: Toward a hierarchical dynamical system framework.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

20 CFR 641.310 - May the Governor delegate responsibility for developing and submitting the State Plan?

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false May the Governor delegate responsibility for developing and submitting the State Plan? 641.310 Section 641.310 Employees' Benefits EMPLOYMENT AND TRAINING... developing and submitting the State Plan, provided that any such delegation is consistent with State law and...

Big Data impacts on stochastic Forecast Models: Evidence from FX time series

Directory of Open Access Journals (Sweden)

Sebastian Dietz

2013-12-01

Full Text Available With the rise of the Big Data paradigm new tasks for prediction models appeared. In addition to the volume problem of such data sets nonlinearity becomes important, as the more detailed data sets contain also more comprehensive information, e.g. about non regular seasonal or cyclical movements as well as jumps in time series. This essay compares two nonlinear methods for predicting a high frequency time series, the USD/Euro exchange rate. The first method investigated is Autoregressive Neural Network Processes (ARNN, a neural network based nonlinear extension of classical autoregressive process models from time series analysis (see Dietz 2011. Its advantage is its simple but scalable time series process model architecture, which is able to include all kinds of nonlinearities based on the universal approximation theorem of Hornik, Stinchcombe and White 1989 and the extensions of Hornik 1993. However, restrictions related to the numeric estimation procedures limit the flexibility of the model. The alternative is a Support Vector Machine Model (SVM, Vapnik 1995. The two methods compared have different approaches of error minimization (Empirical error minimization at the ARNN vs. structural error minimization at the SVM. Our new finding is, that time series data classified as “Big Data” need new methods for prediction. Estimation and prediction was performed using the statistical programming language R. Besides prediction results we will also discuss the impact of Big Data on data preparation and model validation steps. Normal 0 21 false false false DE X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";}

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

Science.gov (United States)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

Directory of Open Access Journals (Sweden)

Ya’nan Wang

2016-01-01

Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

Modelos de gestión de conflictos en serie de ficción televisiva (Conflict management models in television fiction series

Directory of Open Access Journals (Sweden)

Yolanda Navarro-Abal

2012-12-01

Full Text Available Television fiction series sometimes generate an unreal vision of life, especially among young people, becoming a mirror in which they can see themselves reflected. The series become models of values, attitudes, skills and behaviours that tend to be imitated by some viewers. The aim of this study was to analyze the conflict management behavioural styles presented by the main characters of television fiction series. Thus, we evaluated the association between these styles and the age and sex of the main characters, as well as the nationality and genre of the fiction series. 16 fiction series were assessed by selecting two characters of both sexes from each series. We adapted the Rahim Organizational Conflict Inventory-II for observing and recording the data. The results show that there is no direct association between the conflict management behavioural styles presented in the drama series and the sex of the main characters. However, associations were found between these styles and the age of the characters and the genre of the fiction series.

An Illustration of Generalised Arma (garma) Time Series Modeling of Forest Area in Malaysia

Science.gov (United States)

Pillai, Thulasyammal Ramiah; Shitan, Mahendran

Forestry is the art and science of managing forests, tree plantations, and related natural resources. The main goal of forestry is to create and implement systems that allow forests to continue a sustainable provision of environmental supplies and services. Forest area is land under natural or planted stands of trees, whether productive or not. Forest area of Malaysia has been observed over the years and it can be modeled using time series models. A new class of GARMA models have been introduced in the time series literature to reveal some hidden features in time series data. For these models to be used widely in practice, we illustrate the fitting of GARMA (1, 1; 1, δ) model to the Annual Forest Area data of Malaysia which has been observed from 1987 to 2008. The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation.

40 CFR 310.10 - What are temporary emergency measures?

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 27 2010-07-01 2010-07-01 false What are temporary emergency measures... RESPONSE TO HAZARDOUS SUBSTANCE RELEASES Provisions What Can Be Reimbursed? § 310.10 What are temporary... security; (2) Controlling the source of contamination; (3) Containing the release to prevent spreading; (4...

14 CFR 302.310 - Exemptions on the Department's initiative.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Exemptions on the Department's initiative... and Certain Other Proceedings § 302.310 Exemptions on the Department's initiative. The Department may grant exemptions on its own initiative when it finds that such exemptions are required by the...

Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother-Infant Interactions during Infant Immunization

Science.gov (United States)

Stifter, Cynthia A.; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…

State-space prediction model for chaotic time series

Science.gov (United States)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

Time series modeling and forecasting using memetic algorithms for regime-switching models.

Science.gov (United States)

Bergmeir, Christoph; Triguero, Isaac; Molina, Daniel; Aznarte, José Luis; Benitez, José Manuel

2012-11-01

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.

Identification of human operator performance models utilizing time series analysis

Science.gov (United States)

Holden, F. M.; Shinners, S. M.

1973-01-01

The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.

Downsizer - A Graphical User Interface-Based Application for Browsing, Acquiring, and Formatting Time-Series Data for Hydrologic Modeling

Science.gov (United States)

Ward-Garrison, Christian; Markstrom, Steven L.; Hay, Lauren E.

2009-01-01

The U.S. Geological Survey Downsizer is a computer application that selects, downloads, verifies, and formats station-based time-series data for environmental-resource models, particularly the Precipitation-Runoff Modeling System. Downsizer implements the client-server software architecture. The client presents a map-based, graphical user interface that is intuitive to modelers; the server provides streamflow and climate time-series data from over 40,000 measurement stations across the United States. This report is the Downsizer user's manual and provides (1) an overview of the software design, (2) installation instructions, (3) a description of the graphical user interface, (4) a description of selected output files, and (5) troubleshooting information.

Forecasting electricity spot-prices using linear univariate time-series models

International Nuclear Information System (INIS)

Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael

2004-01-01

This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)

Effect of calibration data series length on performance and optimal parameters of hydrological model

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Chuan-zhe Li

2010-12-01

Full Text Available In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments (data are non-continuous and fragmental in some catchments, we used non-continuous calibration periods for more independent streamflow data for SIMHYD (simple hydrology model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization (PSO method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

Stress Rupture and Precipitation Behavior of TP310HCbN(HR3C for Supercritical Boilers

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FANG Xu-dong

2017-06-01

Full Text Available Using persistent experiment method, combined with Gleeble,hardness analysis, SEM, EDS, TEM and other analytical methods, the hot deformation, stress rupture and precipitation behavior of TP310HCbN heat resistance steel was analysed at 650℃ and 700℃, the results show that:the change of the hardness is not obviously under two different stress rupture temperature; with stress rupture time prolongs, TP310HCbN heat-resistant steel intragranular precipitates from granular into rod-shaped, and presence of wormlike NbCrN rich precipitates and dislocation interactions; Taiyuan Iron and Steel production of TP310HCbN heat-resistant steel at 650/700℃-100000h extrapolation lasting strength meet the standard requirements.

Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

DEFF Research Database (Denmark)

Nonejad, Nima

This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast...... and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where...

Applications of soft computing in time series forecasting simulation and modeling techniques

CERN Document Server

Singh, Pritpal

2016-01-01

This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and governmen...

Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

Science.gov (United States)

Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

2017-04-01

Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

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Wuyang Cheng

2014-01-01

Full Text Available We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI and Hang Seng Index (HSI are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models

DEFF Research Database (Denmark)

Yang, Bin; Guo, Chenjuan; Jensen, Christian S.

2013-01-01

of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...

Estimating soil hydraulic properties from soil moisture time series by inversion of a dual-permeability model

Science.gov (United States)

Dalla Valle, Nicolas; Wutzler, Thomas; Meyer, Stefanie; Potthast, Karin; Michalzik, Beate

2017-04-01

Dual-permeability type models are widely used to simulate water fluxes and solute transport in structured soils. These models contain two spatially overlapping flow domains with different parameterizations or even entirely different conceptual descriptions of flow processes. They are usually able to capture preferential flow phenomena, but a large set of parameters is needed, which are very laborious to obtain or cannot be measured at all. Therefore, model inversions are often used to derive the necessary parameters. Although these require sufficient input data themselves, they can use measurements of state variables instead, which are often easier to obtain and can be monitored by automated measurement systems. In this work we show a method to estimate soil hydraulic parameters from high frequency soil moisture time series data gathered at two different measurement depths by inversion of a simple one dimensional dual-permeability model. The model uses an advection equation based on the kinematic wave theory to describe the flow in the fracture domain and a Richards equation for the flow in the matrix domain. The soil moisture time series data were measured in mesocosms during sprinkling experiments. The inversion consists of three consecutive steps: First, the parameters of the water retention function were assessed using vertical soil moisture profiles in hydraulic equilibrium. This was done using two different exponential retention functions and the Campbell function. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil moisture data, which allowed the calculation of the hydraulic conductivity function. Third, the parameters governing flow in the fracture domain were determined using the whole soil moisture time series. The resulting retention functions were within the range of values predicted by pedotransfer functions apart from very dry conditions, where all retention functions predicted lower matrix potentials

Multivariate time series modeling of selected childhood diseases in ...

African Journals Online (AJOL)

This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...

Application of the Laplace transform method for computational modelling of radioactive decay series

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C. [Univ. do Estado do Rio de Janeiro (IME/UERJ) (Brazil). Programa de Pos-graduacao em Ciencias Computacionais

2012-03-15

It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad{sub L} code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)

Application of the Laplace transform method for computational modelling of radioactive decay series

International Nuclear Information System (INIS)

Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C.

2012-01-01

It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad L code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)

A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

Directory of Open Access Journals (Sweden)

Yanbing Liu

2014-01-01

Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

Thermodynamic analysis and economical evaluation of two 310-80 K pre-cooling stage configurations for helium refrigeration and liquefaction cycle

Science.gov (United States)

Zhu, Z. G.; Zhuang, M.; Jiang, Q. F.; Y Zhang, Q.; Feng, H. S.

2017-12-01

In 310-80 K pre-cooling stage, the temperature of the HP helium stream reduces to about 80 K where nearly 73% of the enthalpy drop from room temperature to 4.5 K occurs. Apart from the most common liquid nitrogen pre-cooling, another 310-80 K pre-cooling configuration with turbine is employed in some helium cryoplants. In this paper, thermodynamic and economical performance of these two kinds of 310-80 K pre-cooling stage configurations has been studied at different operating conditions taking discharge pressure, isentropic efficiency of turbines and liquefaction rate as independent parameters. The exergy efficiency, total UA of heat exchangers and operating cost of two configurations are computed. This work will provide a reference for choosing 310-80 K pre-cooling stage configuration during design.

Application of semi parametric modelling to times series forecasting: case of the electricity consumption; Modeles semi-parametriques appliques a la prevision des series temporelles. Cas de la consommation d'electricite

Energy Technology Data Exchange (ETDEWEB)

Lefieux, V

2007-10-15

Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)

Physics constrained nonlinear regression models for time series

International Nuclear Information System (INIS)

Majda, Andrew J; Harlim, John

2013-01-01

A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)

A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

Directory of Open Access Journals (Sweden)

Xiaoping Yang

2016-01-01

Full Text Available The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI prediction, and in severely polluted cases (AQI ≥ 300 the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.

Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study.

Science.gov (United States)

Fang, Xin; Li, Runkui; Kan, Haidong; Bottai, Matteo; Fang, Fang; Cao, Yang

2016-08-16

To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. A time-series study using regional death registry between 2009 and 2010. 8 districts in a large metropolitan area in Northern China. 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models. The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (-1.09 to 4.28 vs -1.08 to 3.93) and the PCs-based model (-2.23 to 4.07 vs -2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, -1.12 to 4.85 versus -1.11 versus 4.83. The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Hybrid perturbation methods based on statistical time series models

Science.gov (United States)

San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

2016-04-01

In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

Science.gov (United States)

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley

An assessment of microstructure, mechanical properties and corrosion resistance of dissimilar welds between Inconel 718 and 310S austenitic stainless steel

International Nuclear Information System (INIS)

Mortezaie, A.; Shamanian, M.

2014-01-01

In the present study, dissimilar welding between Inconel 718 nickel-base superalloy and 310S austenitic stainless steel using gas tungsten arc welding process was performed to determine the relationship between the microstructure of the welds and the resultant mechanical and corrosion properties. For this purpose, three filler metals including Inconel 625, Inconel 82 and 310 stainless steel were used. Microstructural observations showed that weld microstructures for all filler metals were fully austenitic. In tension tests, welds produced by Inconel 625 and 310 filler metals displayed the highest and the lowest ultimate tensile strength, respectively. The results of Charpy impact tests indicated that the maximum fracture energy was related to Inconel 82 weld metal. According to the potentiodynamic polarization test results, Inconel 82 exhibited the highest corrosion resistance among all tested filler metals. Finally, it was concluded that for the dissimilar welding between Inconel 718 and 310S, Inconel 82 filler metal offers the optimum properties at room temperature. - Highlights: • Three filler metals including Inconel 625, Inconel 82 and 310 SS were used. • A columnar to equiaxed dendritic structure was seen for IN-625 weld metal. • A granular austenitic microstructure obtained for Inconel 82 weld metal. • Microstructure of 310 weld metal includes solidification cracks along SSGB. • IN-82 weld metal showed the highest corrosion potential

Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

Science.gov (United States)

Ouyang, Yicun; Yin, Hujun

2018-05-01

Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.