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A curated map of modern time-series forecasting architectures, covering classical baselines, linear models, transformers, diffusion models, and pretrained forecasters. It explains the mechanisms and trade-offs of representative approaches and provides compact, runnable PyTorch implementations, with a short README for each architecture.
1 use taken from transcripts — each links to the moment in the video.
A map of modern time-series forecasting architectures, ranging from classical baselines and linear models to transformers, diffusion models, and pretrained forecasters. It explains mechanisms and trade-offs and includes compact PyTorch implementations for representative models.
1 in the library.