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Hi @marah othman! Here’s a discussion on the topic. In particular, the image attached. You can calculate the strength of the seasonality using the tsfeatures library and computing the

stl_features

function.

are you using in neuralforcast library this feature to train the models ?

You can try Kruskal-Wallis Test:- https://knk00.medium.com/how-to-determine-seasonality-without-plots-f18cee913b95#:~:text=Use%20the%20Kruskal%20function%20by,is%20not%20seasonal%20is%20rejected.

ACF, sort by descending and look at the second coefficient

the second coefficient you mean the second lag ? and could explain why the second coefficient ?

because ACF starts at lag of 0 (first) which will always equal to 1 due to the fact that you are correlating TS with itself. In addition you can look at the index higher than 6 in case of daily data (weekly) and apply custom r-value threshold

mu data is hourly so you told me to see the second and now you told me see the 6 so i am little confuse

what kind of seasonal lag are you expecting to see in your dataset?

how can determine this ?