OPOSSAM: Online Prediction of Stream Data Using Self-adaptive Memory
Akihiro Yamaguchi, Shigeru Maya, Tatsuya Inagi, Ken Ueno · 2018
There is a need for forecasting of short-range future values in data streams such as traffic flows, stock prices, and electricity consumption. However, concept drift in non-stationary data streams is an important problem. We propose an online prediction method called OPOSSAM for such data streams. OPOSSAM manages time-series segments in short-term memory and long-term memory, and forecasts future values by local regression based on the similarity of time-series segments. In particular, OPOSSAM keeps long-term memory consistent by reducing redundant samples with large prediction errors, and automatically adjusts the prediction model based on short-term memory from the prior model learned from the entire memory in order to deal with concept drift. Experimental results show accuracy superior to that of baseline methods on real-world datasets of traffic flow, stock prices, and electricity consumption.