
Learning to embed time series patches independently results in superior time series representations compared to capturing patch dependencies.


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learning-to-embed-time-series-patches-independently-a-novel-approach-for-time-series-representation-learning


Learning to Embed Time Series Patches Independently: A Novel Approach for Time Series Representation Learning


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TSLANet is a novel lightweight convolutional model that leverages adaptive spectral analysis and interactive convolutions to effectively capture both short-term and long-term dependencies in time series data, outperforming state-of-the-art Transformer-based models across various tasks.


tslanet-a-lightweight-and-adaptive-convolutional-model-for-diverse-time-series-tasks


TSLANet: A Lightweight and Adaptive Convolutional Model for Diverse Time Series Tasks
