Prediction to Industrial Added Value Based on Holt-Winters Model and ARIMA Model

DOI

10.2991/amsm-16.2016.49How to use a DOI?

Keywords

ARIMA model; holt-winters model; industrial production; the optimal smoothing coefficient

Abstract

Industrial added value (IAV) is an important indicator to measure a country's industrial development level, at the same time, is also a core indicator of national economy accounting system. The paper respectively using ARIMA and Holt - Winters, the two time series forecasting model, to fit the monthly data of industrial added value of Hubei province in 2009-2014 and on the basis of it we forecast for next year. For ARIMA model, this paper introduces the smooth sequence processing, model identification, parameter estimation and model diagnosis and prediction, such as the key to predict; For Holt-Winters model, this paper introduces the selection of smoothing coefficient alpha, beta, gamma and initial smoothing factor, recursive calculation process, such as the key to predict, and the corresponding algorithm and the prediction model is designed. Finally use example to compare the two methods and analyze the pros and cons of two kinds of models to predict.

Copyright

© 2016, 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  - Lei Kuang
AU  - Chengyu Lin
AU  - Wenwen Wang
AU  - Xi Fang
PY  - 2016/05
DA  - 2016/05
TI  - Prediction to Industrial Added Value Based on Holt-Winters Model and ARIMA Model
BT  - Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling
PB  - Atlantis Press
SP  - 214
EP  - 218
SN  - 2352-538X
UR  - https://doi.org/10.2991/amsm-16.2016.49
DO  - 10.2991/amsm-16.2016.49
ID  - Kuang2016/05
ER  -