Multilayered Cortical Learning Algorithm for Forecasting Time-Series Data with Probabilistically Changing Trends

In this paper, a burst-based multilayered cortical learning algorithm (BM-CLA) for forecasting trend-changing time-series data is proposed. CLA predicts time-series data while adjusting synapse relationships online. However, the forecast accuracy of the conventional CLA deteriorates with trend-changing time-series data, in which several time-series trends are switched over time. The proposed BM-CLA detects trend changes based on multilayered CLA predictors. Experimental results using multiple artificial time-series data with probabilistically changing trends showed that BM-CLA achieves results that are better or comparable to those of conventional CLAs with different specifications and the long short-term memory (LSTM), which is a neural network-based forecast algorithm.