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Time series analysis is used to understand and predict dynamic processes,including evolving demands in business, weather, markets, and biologicalrhythms. Exponential smoothing is used in all these domains to obtain simpleinterpretable models of time series and to forecast future values. Despite itspopularity, exponential smoothing fails dramatically in the presence ofoutliers, large amounts of noise, or when the underlying time series changes.  We propose a flexible model for time series analysis, using exponentialsmoothing cells for overlapping time windows. The approach can detect andremove outliers, denoise data, fill in missing observations, and providemeaningful forecasts in challenging situations. In contrast to classicexponential smoothing, which solves a nonconvex optimization problem over thesmoothing parameters and initial state, the proposed approach requires solvinga single structured convex optimization problem. Recent developments inefficient convex optimization of large-scale dynamic models make the approachtractable. We illustrate new capabilities using synthetic examples, and thenuse the approach to analyze and forecast noisy real-world time series. Code forthe approach and experiments is publicly available.

Time Series Using Exponential Smoothing Cells