Unconditional Training in TSDiff Enhances Robustness Against Domain Shifts in Time Series Forecasting via KL Divergence on ETTm2

Abstract

Abstract: A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can cope with complex and high-dimensional time series that cannot be usually handled by other machine learning techniques. The aim of the work is to provide a review of state-of-the-art deep learning architectures for time series forecasting, underline r

Research Question

To what extent does the unconditional training approach in TSDiff improve robustness against domain shifts in time series forecasting compared to conditional diffusion models, evaluated using KL divergence between predicted and ground truth distributions on the ETTm2 benchmark?

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