Chronos: Learning the Language of Time Series with Abdul Fatir Ansari - #685
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Enhancing Time Series Forecasting with Data Augmentation
This chapter examines the importance of data generation and augmentation in improving time series forecasting models. It contrasts the challenges posed by limited high-quality time series data with the availability of datasets in language modeling, detailing specific augmentation techniques like TS Mixup and Gaussian processes. The discussion includes insights on the efficacy of combining real and synthetic data for training, along with the evaluation methods used to benchmark model performance.
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