A DFS Model for Forecasting Stock Price

DOI

10.2991/wartia-16.2016.334How to use a DOI?

Keywords

DFS model, time series model, combination forecast model, wavelet analysis, Fourier transform, fitting analysis

Abstract

Currently, forecasting stock price is the hotter topic for achieving the smallest lost in investment. However, the previous stock price forecasting model practically cannot satisfy the requirement of accuracy. To raise the forecast accuracy, a decomposition-forecast- synthesis (DFS) model is proposed by this paper, based on the analysis of the characteristics of the stock price time series, combined with the established single stock price prediction model, for instance, time series model, grey prediction model, neural network prediction model, etc.

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/).

Download article (PDF)

TY  - CONF
AU  - Xiaolu Li
AU  - Hanghang Zhao
AU  - Kaiqiang Zheng
AU  - Shuaishuai Sun
PY  - 2016/05
DA  - 2016/05
TI  - A DFS Model for Forecasting Stock Price
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 1676
EP  - 1681
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.334
DO  - 10.2991/wartia-16.2016.334
ID  - Li2016/05
ER  -