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I was researching about using deep learning for time series forecasting applications when I came across two experiments by the Nixtla team. They showed that their traditional statistical ensemble (comprised of AutoARIMA, ETS, CES, and DynamicOptimizedTheta) beat a bunch of deep learning models (link) and also the AWS forecast API (link).
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[D] When less is more in the hierarchical forecasting case.
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Time series and cross validation
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Show HN: Probabilistic hierarchical forecasting with statistical methods
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Sh: Probabilistic hierarchical forecasting with statistical methods
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Probabilistic and nonnegative methods for hierarchical forecasting in python are now available in Nixtla's HierachicalForecast