A dynamic linear model approach for disaggregating time series data

Bayesian dynamic linear model is a promising method for time series data analysis and short-term forecasting. One research issue concerns how the predictive model adapts to changes in the system, especially when shocks impact system behavior. In this study, we propose an adaptive dynamic linear model to adaptively update model parameters for online system state prediction. The proposed method is an automatic approach based on the feedback of prediction errors at each time slot without the needs of external intervention. The experimental study on short-term travel speed prediction shows that the proposed method can significantly reduce the prediction errors of the traditional dynamic linear model and outperform two state-of-the-art methods in the case of major system behavior changes.

Download Full-text

Generalized Linear Model with Time Series Data

Korean Journal of Applied Statistics ◽

10.5351/kjas.2003.16.2.365 ◽

2003 ◽

Vol 16 (2) ◽

pp. 365-376 ◽

Cited By ~ 2

Keyword(s):

Time Series ◽

Linear Model ◽

Generalized Linear Model ◽

Time Series Data ◽

Series Data

Download Full-text

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.

Download Full-text

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

Download Full-text

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.

Download Full-text