In this blog post, we experiment with a hierarchical exponential smoothing forecasting model, extending the ideas from the univariate case presented in the blog post “Notes on Exponential Smoothing with NumPyro”. We use NumPyro and compare the NUTS and SVI results. For such a purpose, we use Continuous Ranked Probability Score (CRPS). We also compare these forecasts with univariate statistical models like Holt-Winters, AutoETS and Seasonal Naive from the great Statsforecast package. These baseline models are, in general, hard to beat!
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In this blog post, we experiment with a hierarchical exponential smoothing forecasting model, extending the ideas from the univariate case presented in the blog post “Notes on Exponential Smoothing with NumPyro” . We use NumPyro and compare the NUTS and SVI results. For such a purpose, we use Continuous Ranked Probability Score ( CRPS ). We also compare these forecasts with univariate statistical…
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