Main function is predict in predictit main.py script. There is also predict_multiple_columns function if you want to predict more at once (columns or time frequencies) and also compare_models function that tell you which models are best. It evaluates error criterion on out of sample test data instead of predict (which use as much data as possible). Some models, predictit for example decision trees just assign input from learning set, so error in predict is 0, in compare_models its accurate. So first is optimal to use compare_models , find best models and then use it in predict. Try live demo - playground on binder. Config. and type config. It is basically hard to keep observe of your expenses if you finish up traveling to totally different countries which have predictit completely different currencies.
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Example is here. Main benefit is performance boost. You can have code under the much simpler (much less code), but no features from configuration available. Example of using library as a pro with deeper editing config. Time series prediction is very different from image recognition and more data doesn't necessarily mean better prediction. If you're issuing performance problems, try fast preset (turn off optimizations, make less predictit recurrent values, choose only few models, threshold datalength etc.) you can edit preset if you need. If you still have performance troubles, and you have too much data, use resampling and select only valuable columns - for example correlation_threshold and do not derive extra columns. Bolluk bereket duası.
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