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Essentially you want to pick a function that will give you the "size" of a matrix. The most obvious way I can think of is by choosing a matrix norm, which is a map $\lVert \cdot \rVert \colon \mathbb{R}^{k, k} \to [0, \infty)$ (or you could generalise to a complex $k \times k$ matrix if you wished). Your suggestion seems similar to computing S = \...

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I will use the "date" and "time" columns to pre-process your data and to construct your neural net input. RNN does not work well for very long-term dependancies... so, for example, creating a time series with all minutes in a month, won't probably work. You must select: How many samples your input data will have What is your sampling ...

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I think that indeed you may have leakage by using pd.qcut. A solution to avoid that leakage is to do it in a time-series cross-validation fashion. The idea is to derive the quantile values in a training fold, and, with those values, do the cut in its validation fold. This is a little complex and if you want it simpler you can use pd.cut, which will have no ...

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Using the data from FRED for both labour data and recession data and slight adjustments to your code I think I get the result you want: import pandas as pd import matplotlib.pyplot as plt # read in data rec_data = pd.read_csv("USREC.csv") ls_data = pd.read_csv("LABSHPUSA156NRUG.csv") # make sure date columns are actual dates rec_data[&...

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Welcome to the community Fra, below you can find a worked out example implementing a multivariate several input features (as I think is your case) time series forecasting, predicting multiple future steps (multi-step forecast), applying bayesian hyperparametrization. It is based on a from simpler to more complex approach, so you can see there are few layers ...

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As @10xAI said, a tree-based gradient boosted approach may miss the mark for time series because it cannot forecast a growing trend. However, we can apply gradient boosting methodology to any algorithm. You can mess around with some code I wrote that is based on gradient boosting and decomposition: LazyProphet. The code is badly written and I think the ...

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You can treat the markets as a categorical feature in a tree based (using decision tree as a weak leaner) ensemble model such as random forest or gradient boosting. Some applications are: Sales for retail outlets of a major European Pharmacy retailing company Predicting the sales of products from different outlets Multi-Store multi-product forecasting

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