Learning Latent Seasonal-Trend Representations for Time Series Forecasting

Nov 28, 2022



Forecasting complex time series is ubiquitous and vital in a range of applications but challenging. Recent advances endeavor to achieve progress by incorporating various deep learning techniques (e.g., RNN and Transformer) into sequential models. However, clear patterns are still hard to extract since intricate time series are composed of several entangled components. Motivated by the success of disentangled variational autoencoder in computer vision and classical time series decomposition, we plan to infer a couple of representations that depict seasonal and trend components of time series. To achieve this goal, we propose LaST, which, based on variational inference, aims to disentangle the seasonal-trend representations in the latent space. Furthermore, LaST supervises and disassociates representations from the perspectives of themselves and input reconstruction, and introduces a series of auxiliary objectives. Extensive experiments prove that LaST achieves state-of-the-art performance on time series forecasting task with 23.7


Store presentation

Should this presentation be stored for 1000 years?

How do we store presentations

Total of 1 viewers voted for saving the presentation to eternal vault which is 0.1%


Recommended Videos

Presentations on similar topic, category or speaker