The article explores advancements in applying transformers to time series data by segmenting data into patches, significantly improving representation efficiency. While traditional methods relied on contrastive learning, which is often subjective, masked representation learning emerges as a viable alternative for forecasting and imputation tasks. This method allows for model training using masked portions of time series data, drawing parallels to successful techniques in language and image processing. The study emphasizes the potential of masked representation learning in fostering effective forecasting models in time series analysis.
The transformer architecture can efficiently learn representations for time series data by treating time series sub-sequences as tokens, significantly simplifying the modeling process.
Contrastive representation learning has limitations due to its reliance on subjective data augmentation, while masked representation learning offers a more straightforward approach to tackle time series forecasting.
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