Non‐stationary financial time series forecasting based on meta‐learning

Abstract

Abstract In this letter, the authors address the challenge in forecasting non‐stationary financial time series by proposing a meta‐learning based forecasting model equipped with a convolution neural network (CNN) predictor and a long short‐term memory (LSTM) meta‐learner. The model is applied to a set of short subseries which are the result of dividing a long non‐stationary financial time series. As a result, a promising performance can be achieved by the proposed model in terms of making more accurate prediction than the traditional CNN predictor and auto regressive (AR)‐based forecasting models in non‐stationary conditions.

Keywords

• convolutional neural nets
• economic forecasting
• learning (artificial intelligence)
• neural nets
• time series