CRAFT: Time Series Forecasting with Cross-Future Behavior Awareness
Keywords: Time Series Forecasting, Cross-Future Behavior, Koopman Theory, Hierarchical Structure
Abstract: Time series forecasting is the crucial infrastructure in the field of e-commerce, providing technical support for consumer behavior analysis, sales trends forecasting, etc. E-commerce allows consumers to reserve in advance. These pre-booking features reflect future sales trends and can increase the certainty of time series forecasting issues. In this paper, we define these features as Cross-Future Behavior, which occurs before the current time but takes effect in the future. To increase the performance of time series forecasting, we leverage these features and propose the CRoss-Future Behavior Awareness based Time Series Forecasting method (CRAFT). The core idea of CRAFT is to utilize the trend of cross-future behavior to mine the trend of time series data to be predicted. Specifically, to settle the sparse and partial flaws of cross-future behavior, CRAFT employs the Koopman Predictor Module to extract the key trend and the Internal Trend Mining Module to supplement the unknown area of the cross-future behavior matrix. Then, we introduce the External Trend Guide Module with a hierarchical structure to acquire more representative trends from higher levels. Finally, we apply the demand-constrained loss to calibrate the distribution deviation of prediction results. We conduct experiments on real-world dataset. Experiments on both offline large-scale dataset and online A/B test demonstrate the effectiveness of CRAFT. Our dataset and code will be released after formal publication.
Primary Area: learning on time series and dynamical systems
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Submission Number: 1100
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