Multi-layer Cortical Learning Algorithm for Forecasting Time-series Data with Smoothly Changing Variation Patterns
Kazushi Fujino, Keiki Takadama, Hiroyuki Sato · 2024
This paper introduces the Decay Burst-based Multi-layered Cortical Learning Algorithm (Decay-BM-CLA), designed for forecasting time-series data with smoothly changing variation patterns. CLA is a neocortex-inspired forecasting algorithm that predicts time-series data by dynamically adjusting the states of memory and linking elements within the predictor. An extended version, BM-CLA, has a dual CLA predictor comprising the upper and lower layers to handle multiple variation patterns. The lower layer receives time-series data, while the upper layer detects changes in variation patterns based on the success or failure of predictions in the lower layer and adjusts the states in the lower layer to fit the current variation pattern. However, conventional BM-CLA faces difficulty in accurately forecasting time-series data with smoothly changing variation patterns. This limitation arises from its mechanism, which alters the states in the upper layer only when detecting a change in variation patterns. The states representing the pattern are then maintained until the next detection. In response, the proposed Decay-BM-CLA determines the states in the upper layer using active strengths that decay at every time step. Consequently, the states in the upper layer gradually change as time progresses, facilitating the adjustment of states in the lower layer to smoothly match the changing variation patterns in the data. This paper conducts experiments using artificial time-series data with smoothly changing variation patterns and real-world time-series data, including seasonal variations such as electricity consumption and temperature variations. Experimental results demonstrate that the proposed Decay-BM-CLA outperforms a neural network-based Online-LSTM, the latest FSNet, and conventional CLA variants in terms of prediction accuracy on these time-series data.