Hi @Max (Nixtla) Just sharing the questions here: Error measure questions • What's a reliable error measure for evaluating models at the item level, given our data is extremely intermittent? • Why is the gap between poor and good methods small when calculating error measure at the item level, but becomes larger when we aggregate forecast to a higher level? • How should we account for in-stock and out-of-stock periods in forecast accuracy calculation at the item level? Clustering questions: • What's the most effective approach for clustering time series data that includes both slow and fast-moving items to identify suitable forecasting methods? • What are the key features we should consider to classify time series, allowing for the use of distinct models for each category in the presence of intermittency?

Hi @Arsa Nikzad, I just read some articles that might be interesting for the error measures. For the medium and towardsdatascience articles, if it doesn't work you can try "private browsing" in your browser. https://towardsdatascience.com/forecast-kpi-rmse-mae-mape-bias-cdc5703d242d https://nicolas-vandeput.medium.com/how-to-forecast-intermittent-products-c5d477b90176