eth-siplab/Shift-Invariant_Deep_Learning_on_Time_Series
The official implementation of ICLR2025 paper for shift-invariant neural networks
This project helps data scientists, machine learning engineers, and researchers working with time series data improve the reliability and accuracy of their deep learning models. It takes raw time series data, processes it through a novel transformation, and outputs predictions that are consistent even if the timing of events in the input data shifts. This means your models will perform better on tasks like activity recognition, heart rate prediction, and disease classification.
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Use this if your deep learning models for time series data are sensitive to slight shifts in the timing of events and you need to achieve consistent performance regardless of these shifts.
Not ideal if your primary focus is on image processing or other data types, as this solution is specifically designed for time series analysis.
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Last pushed
Apr 27, 2025
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