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Poster
SSMF: Shifting Seasonal Matrix Factorization
Koki Kawabata · Siddharth Bhatia · Rui Liu · Mohit Wadhwa · Bryan Hooi

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @
in Poster Session 2 »

Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands? In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events? In this paper, we propose Shifting Seasonal Matrix Factorization approach, namely SSMF, that can adaptively learn multiple seasonal patterns (called regimes), as well as switching between them. Our proposed method has the following properties: (a) it accurately forecasts future events by detecting regime shifts in seasonal patterns as the data stream evolves; (b) it works in an online setting, i.e., processes each observation in constant time and memory; (c) it effectively realizes regime shifts without human intervention by using a lossless data compression scheme. We demonstrate that our algorithm outperforms state-of-the-art baseline methods by accurately forecasting upcoming events on three real-world data streams.

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Author Information

Koki Kawabata (Osaka University)
Siddharth Bhatia (National University of Singapore)
Rui Liu (National University of Singapore)
Mohit Wadhwa (LinkedIn)
Bryan Hooi (National University of Singapore)

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