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Zero-Shot Forecasting: Our Search for a Time-Series Foundation Model
Introduction In the last few years, the field of time-series forecasting has seen a fundamental shift. Where we once depended solely on classic statistical methods, think ARIMA, SARIMA, and Prophet, new “foundation” models have emerged, promising to ...
Running a separate, hand-tuned forecasting model for every slice quickly turns into a treadmill: each new stream or workload tweak demands fresh hyper-params, retrains, and ever-growing config sprawl. Despite their compact size, these models are trained on diverse datasets and often deliver surprising accuracy, making them a good fit for IoT, embedded devices, or any observability scenario where every CPU cycle counts. Chronos worked well across diverse pods, while IBM’s “Tiny Time-Mixer” was especially notable for its efficiency, delivering decent accuracy with minimal compute, making it a great fit for edge or cost-sensitive scenarios.
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