A Study on Prediction the Movement Pattern of Time Series Data using Information Criterion and Effective Data Length

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

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JUDGMENT ALGORITHM FOR PERIODICITY OF TIME SERIES DATA BASED ON BAYESIAN INFORMATION CRITERION

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720008003722 ◽

2008 ◽

Vol 06 (04) ◽

pp. 747-757 ◽

Cited By ~ 2

Author(s):

DAISUKE TOMINAGA ◽

KATSUHISA HORIMOTO

Keyword(s):

Time Series ◽

Bayesian Information Criterion ◽

Time Series Data ◽

Information Criterion ◽

Series Data ◽

Experimental Conditions ◽

Biological Time ◽

Circadian Expression ◽

Expression Of Genes ◽

Higher Sensitivity

Judgment periodicity of biological time series data is important and done widely to find the circadian expression of genes, monthly change of hormones, etc. To keep complete reproducibility of judgment is a problem because popular judgment methods such as curve fitting, Fourier analysis, etc. need judgment criteria determined by analysts considering experimental conditions and results (level, S/N, distribution, etc.) based on their experience. Judgment results are often affected by analysts' subjects. Reproducible criterion determination is therefore strongly needed. We propose introducing the information criterion to replace analysts' criteria. A judgment algorithm by combining Bayesian information criterion (BIC) and discrete Fourier transform (DFT) has been developed and has proved its ability through application to mice microarray data and finding of circadian genes. Our method, named "Piccolo", shows higher sensitivity than the simple DFT (without BIC) method with reproducibility, and can be fully automated.

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Predicting Rice Production using Autoregressive Integrated Moving Average Model

Journal of AgriSearch ◽

10.21921/jas.v6i04.16905 ◽

2019 ◽

Vol 6 (04) ◽

Author(s):

R C BHARATI ◽

ANIL KUMAR SINGH

Keyword(s):

Time Series ◽

Root Mean Square Error ◽

Mean Square Error ◽

Time Series Data ◽

Moving Average ◽

Information Criterion ◽

Rice Production ◽

Series Data ◽

Mean Square ◽

Autoregressive Integrated Moving Average

A study was conducted on time-series data on rice production in India. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) time-series process was considered for predicting country's rice production using the time series data from 1950–51 to 2017–18. Data from 1950–51 to 2014–15 were used for model development and three years data from 2015–16 and 2017–18 were kept for validation The augmented Dicky Fuller test was applied to test stationarity in data set. Root mean square error. Based on ACF and PACF, the model was defined and tested for its suitability. Akaike information criterion and Bayesian information criterion were used to judge the suitability of the model to be fitted. The performance of the fitted model was examined using mean absolute error, mean percent forecast error, root mean square error and Theil's inequality coefficients. IMA (0, 1, 1) model performed well for forecasting purposes. The percent prediction error for the last three years i.e. from 2015–16 and 2017–18, was below 3%. The predicted values along with their standard errors up to the year 2099, were also obtained using the model.

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Model selection in time series analysis: using information criteria as an alternative to hypothesis testing

Journal of Economic Studies ◽

10.1108/jes-09-2020-0469 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

R. Scott Hacker ◽

Abdulnasser Hatemi-J

Keyword(s):

Time Series ◽

Model Selection ◽

Hypothesis Testing ◽

Time Series Data ◽

Information Criterion ◽

Response Surfaces ◽

Series Data ◽

Content Type ◽

Testing Strategies ◽

Alternative Approach

PurposeThe issue of model selection in applied research is of vital importance. Since the true model in such research is not known, which model should be used from among various potential ones is an empirical question. There might exist several competitive models. A typical approach to dealing with this is classic hypothesis testing using an arbitrarily chosen significance level based on the underlying assumption that a true null hypothesis exists. In this paper, the authors investigate how successful the traditional hypothesis testing approach is in determining the correct model for different data generating processes using time series data. An alternative approach based on more formal model selection techniques using an information criterion or cross-validation is also investigated.Design/methodology/approachMonte Carlo simulation experiments on various generating processes are used to look at the response surfaces resulting from hypothesis testing and response surfaces resulting from model selection based on minimizing an information criterion or the leave-one-out cross-validation prediction error.FindingsThe authors find that the minimization of an information criterion can work well for model selection in a time series environment, often performing better than hypothesis-testing strategies. In such an environment, the use of an information criterion can help reduce the number of models for consideration, but the authors recommend the use of other methods also, including hypothesis testing, to determine the appropriateness of a model.Originality/valueThis paper provides an alternative approach for selecting the best potential model among many for time series data. It demonstrates how minimizing an information criterion can be useful for model selection in a time-series environment in comparison to some standard hypothesis testing strategies.

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Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model

International Journal of Financial Research ◽

10.5430/ijfr.v8n3p154 ◽

2017 ◽

Vol 8 (3) ◽

pp. 154

Author(s):

Kaiying Sun

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Model Fitting ◽

Garch Model ◽

Information Criterion ◽

Equity Returns ◽

Series Data ◽

Arima Models ◽

Fitting Procedure

In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.

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Dynamic Modelling and Forecasting of Data Export of Agricultural Commodity by Vector Autoregressive Model

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.3.41 ◽

2020 ◽

Vol 55 (3) ◽

Author(s):

L.M. Hamzah ◽

S.U. Nabilah ◽

E. Russel ◽

M. Usman ◽

E. Virginia ◽

...

Keyword(s):

Time Series ◽

Statistical Models ◽

Time Series Data ◽

Information Criterion ◽

Series Data ◽

Var Model ◽

Vector Autoregressive Model ◽

Vector Autoregressive ◽

Coffee Beans ◽

The Relationship

The Vector Autoregressive Model (VAR) is one of the statistical models that can be used for modeling multivariate time series data. It is commonly used in finance, management, business and economics. The VAR model analyzes the time series data simultaneously to arrive at the right conclusions while dynamically explaining the behavior of the relationship between endogenous variables, as well as endogenous and exogenous variables. From time to time, the VAR model is influenced by its own factors via Granger Causality. In this study, we will discuss and determine the best model to describe the relationship among data export value of Indonesia's agricultural commodities—coffee beans, cacao beans and tobacco—where the monthly data spans the years 2007-2018. Several models are applied to the data, such as VAR (1), VAR (2), VAR (3), VAR (4) and VAR (5) models. As a result, the VAR (2) model was chosen as the best model based on the Akaike’s Information Criterion with Correction, Schwarz Bayesian Criterion, Akaike’s Information Criterion and Hanna-Quinn Information Criterion for selecting statistical models. The dynamic behavior of the three export variables of Indonesian coffee beans, cacao beans and tobacco is explained by Granger Causality. Furthermore, the best model VAR (2) is used to forecast the next 10 months.

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UJI LINEARITAS BERDASARKAN ESTIMASI MEAN DAN VARIANSI BERSYARAT UNTUK PROSES RUNTUN WAKTU

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2009.1.1.2976 ◽

2009 ◽

Vol 1 (1) ◽

pp. 31

Author(s):

Supriyanto Supriyanto ◽

Herni Utami

Keyword(s):

Time Series ◽

Linear Model ◽

Test Data ◽

Akaike Information Criterion ◽

Time Series Data ◽

Information Criterion ◽

Series Data ◽

Test Statistics ◽

Bootstrap Procedure ◽

Order Estimation

This study aims to examine the benefits of testing linearity in the case of live test data. Bootstrap procedure is used to form the estimators of the statistics. Hypothetical form is used to follow the linear model. And compare the value of criticism from the distribution of this value with the test statistics that have been calculated based on the observed time series data existing. This procedure starts with a model determines autoregression to the data. By using the Akaike information criterion, order estimation obtained from the autoregression models.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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