Recentness biased learning for time series forecasting

In recent years, dynamic time series analysis with the concept drift has become an important and challenging task for a wide range of applications including stock price forecasting, target sales, etc. In this paper, a recentness biased learning method is proposed for dynamic time series analysis by introducing a drift factor. First of all, the recentness biased learning method is derived by minimizing the forecasting risk based on a priori probabilistic model where the latest sample is weighted most. Secondly, the recentness biased learning method is implemented with an autoregressive process and the multi-layer feed-forward neural networks. The experimental results have been discussed and analyzed in detail for two typical databases. It is concluded that the proposed model has a high accuracy in time series forecasting.