A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series
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Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold-start problem seen in recommender systems which, to our knowledge, has not been directly addressed in previous work. In addition, modern time series datasets are often plagued by missing data. We focus on forecasting seasonal profiles---or baseline demand---for periods on the order of a year long, even in the cold-start setting or with otherwise missing data. Traditional time series approaches that perform iterated step-ahead methods struggle to provide accurate forecasts on such problems, let alone in the missing data regime. We present a computationally efficient framework which combines ideas from high-dimensional regression and matrix factorization on a carefully constructed data matrix. Key to our formulation and resulting performance is (1) leveraging repeated patterns over fixed periods of time and across series, and (2) metadata associated with the individual series. We provide analyses of our framework on large messy real-world datasets.
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