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As far as estimating similarity between the time series series there is a variety of methods you may want to investigate. Some of those are: cross correlation: this will be affected by the amplitude and will not be able to estimate lagged correlations, prone to noise. coherence: normalised frequency based correlation (cross-spectrum), not prone to amplitude ...


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For this type of issue, I typically add the reciprocal of the log base. For data that's being log10-scaled, this results in adding 0.1 to all values. For data that's being log2-scaled, this results in adding 0.5 to all values. This has the nice property of mapping all of your 0 values to -1 in the log scale, regardless of what log base you use. If your data ...


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Your suggestion is a valid one, encoding variables with a known outcome once the scaling is applied. Log(1) will become zero, so just keep that in mind for your next stage. You can use clip or replace for this: df.clip(1, df.max()) or try replacing with a NaN df.replace(0, np.nan) Alternatively you could do one of the following: Drop the zero value rows e....


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It completely depends on the type of model. Some models need to represent the features with parameters: for example Naive Bayes with numerical features needs to have a way to calculate the probability based on the value, and the most common case is to assume that the features follow a normal distribution. On the other hand whether a feature is normally ...


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For better control over the x-axis formatting, you can use the matplotlib.dates methods. In your case, MonthLocator and DateFormatter could be of interest. These can be used to adjust the x-axis as follows: import matplotlib.dates as mdates def time_series(start, end): time_series_df = df.loc[(df['Date'] >= start) & (df['Date'] <= end), ['...


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