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Activity Number: 301
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #320152 View Presentation
Title: Unsupervised Anomaly Detection in Time Series with Application in Electricity Demand Forecasting
Author(s): Bei Chen* and Mathieu Sinn and Ulrike Fischer
Companies: IBM Research and IBM Research and IBM Research
Keywords: anomaly detection ; forecasting ; energy demand prediction ; unsupervised learning

An efficient anomaly detection in the training data is crucial for achieving high forecasting accuracy. When dealing with a large number of time series, it is not feasible to clean each series manually, especially when model re-training is periodically required. In this talk I will present several automatic anomaly detection algorithms for big noisy data, with their applications in electricity demand forecasting. In particular, I will discuss how to remove outliers within the electricity demand context while retaining meaningful extreme values, and how to select an optimal training period.

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

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