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Research on Time Series Prediction Algorithm Improved by Markov Model

  

  1. 1. Wuhan Research Institute of Posts and Telecommunications, Wuhan 430074, China；
      2. FiberHome StarrySky Co. Ltd., Nanjing 210019, China
  • Received:2014-09-05 Online:2014-11-27 Published:2014-12-10

Abstract:

The traditional time series prediction algorithm can well simulate and predict the stable time series data, but not so well to the series of nonlinear and non
-stationary. To solve this problem, an improved algorithm comes up. Through the wavelet decomposition and single reconstruction, the original time series is decomposed into a
layer of low frequency data and two layers of high frequency data. The GARCH model is used to forcast the low frequency data, the improved algorithm is used to forecast the two
layers of high frequency data. Through Markov model predicting the state interval, with the smoothing coefficient, the high frequency data is predicted. The final forecasting
result comes from the superposition of the three layers of prediction result. Through the error test, the accuracy of the improved algorithm has a major improvement.

Key words: prediction algorithm, time series, wavelet analysis, Markov model, smoothing coefficient