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Has anyone else been diving deep into the "Guide to Mastery: Step-by-Step Process for Building accurate TrendTapestry Models"? I'm finding the detailed breakdown of data cleaning and feature engineering notably useful. Its definitely helped me move beyond just throwing raw data at the algorithm and hoping for the best.
I'm curious to hear if others have found certain steps more challenging than others. For me, identifying the optimal lookback period for my specific dataset has been a bit of a struggle. The guide offers some good starting points, but I'm still experimenting with different lengths and evaluation metrics to find what works best.
Also, what are your thoughts on the section covering model validation? I'm using a combination of techniques outlined in the guide (cross-validation, out-of-sample testing), but always looking for ways to improve the robustness of my models. Any tips or resources you all have found helpful would be greatly appreciated!