Robust Prediction in Nearly Periodic Time Series Using Motifs

Abstract

In this paper, we consider the prediction task for a process with nearly periodic property, i.e., patterns occur with some regularities but no exact periodicity. We propose an inference approach based on probabilistic Markov framework utilizing motif-driven transition probabilities for sequential prediction. In particular, a Markov-based weighting framework utilizing fully the information from recent historical data and sequential pattern regularities is developed for nearly periodic time series prediction. Preliminary experimental results show that our prediction approach is competitive against the moving average and multi-layer perceptron neural network approaches on synthetic data. Moreover, our proposed method is shown to be empirically robust on time-series with missing data and noise. We also demonstrate the usefulness of our proposed approach on a real-world vehicle parking lot availability prediction task.

Keywords: Time series analysis, Predictive models, Markov processes, Prediction algorithms, Robustness, Neural networks, Noise