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Activity Number: 576 - Advanced Methodological Contributions in Time Series and Forecasting
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #330331
Title: Robust Time Series Using Linked Exponential Smoothing Cells
Author(s): Aleksandr Aravkin* and Avner Abrami and Younghun Kim
Companies: University of Washington, Seattle and IBM TJ Watson Research Center and Utopus Insights
Keywords: robust methods; time series; holt-winters; kalman smoothing; convex optimization

Exponential smoothing decomposes time series into interpretable components (such as level, trend, and seasonality), and is used to understand and to forecast time series is weather prediction, financial markets, energy demands, and indicators of economic stability. Outliers, missing data, and nonstationary series make it difficult to reliably forecast trends and associated uncertainty.

We develop a new time series model, by linking together exponential smoothing cells in a dynamic framework. The resulting approach uses robust losses and regularization to handle outliers and high levels of noise, detect evolving trends, interpolate missing data, and improve forecasting. The new model is fit by solving a single convex optimization problem.

Authors who are presenting talks have a * after their name.

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