A modular memory framework for time series prediction

Abstract

Tangled Program Graphs (TPG) is a framework for genetic programming which has shown promise in challenging reinforcement learning problems with discrete action spaces. The approach has recently been extended to incorporate temporal memory mechanisms that enable operation in environments with partial-observability at multiple timescales. Here we propose a highly-modular memory structure that manages temporal properties of a task and enables operation in problems with continuous action spaces. This significantly broadens the scope of real-world applications for TPGs, from continuous-action reinforcement learning to time series forecasting. We begin by testing the new algorithm on a suite of symbolic regression benchmarks. Next, we evaluate the method in 3 challenging time series forecasting problems. Results generally match the quality of state-of-the-art solutions in both domains. In the case of time series prediction, we show that temporal memory eliminates the need to pre-specify a fixed-size sliding window of previous values, or autoregressive state, which is used by all compared methods. This is significant because it implies that no prior model for a time series is necessary, and the forecaster may adapt more easily if the properties of a series change significantly over time.