Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
Author:
Affiliation:
1. Zhejiang University, Hangzhou, China
2. State Street Technology (Zhejiang) Ltd., Hangzhou, China
3. Salesforce Research Asia, Singapore, Singapore
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671926
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3. Sercan O. Arik Nathanael C. Yoder and Tomas Pfister. 2022. Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. arxiv: 2202.02403 [cs.LG]
4. Francis Bach. 2023. Learning theory from first principles. (2023).
5. A review on time series forecasting techniques for building energy consumption
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