Application of State-Space Model to Exact Time Series Forecasting

DOI

10.2991/meic-14.2014.253How to use a DOI?

Keywords

State space model; ARIMA; time series analysis; exact forecasting; control engineering

Abstract

The forecasting method of future values of a time series from current and past values is of considerable practical interest and in important areas of application. In addition to calculating the best forecasts, it is also necessary to specify their accuracy, so that the risks associated with decisions based upon the forecasts may be calculated. Many empirical time series behave as though they had no fixed mean. They exhibit homogeneity in the sense that apart from local level, or perhaps local level and trend, one part of the series behaves much like any other part. Models that describe such homogeneous nonstationary behavior can be obtained by supposing some suitable difference of the process to be stationary. There has been much recent interest in the representation of ARIMA models in the state-space form, for purposes of forecasting, as well as for model specification and maximum likelihood estimation of parameters. In this paper we briefly consider the state-space form of an ARIMA model in this section and discuss its uses in exact finite sample forecasting.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Changjiang Zheng
AU  - Yujin Dong
AU  - Youxiang Cui
AU  - Fuji Xie
PY  - 2014/11
DA  - 2014/11
TI  - Application of State-Space Model to Exact Time Series Forecasting
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 1135
EP  - 1138
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.253
DO  - 10.2991/meic-14.2014.253
ID  - Zheng2014/11
ER  -