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Highlights

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. All that manual churn slows forecasts and breeds drift, so the results never feel fully trustworthy. Then came the rise of foundation models, which revolutionised natural language processing by offering strong zero-shot and transfer learning capabilities. (View Highlight)