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I am trying to learn more about multivariate time series forecasting. I want to predict refugee arrival in Europe depending on weather data. The arrival data is from a different source and the weather data is also. Is there a way to combine those two in order to have a dataset for Multivariate time series forecasting? 1 Time Series: Monthly Arrival Data 2 Time Series: Weather Data

Would it be possible to make one data frame with the weather data and the arrival data to use this for multivariate time series forecasting?

For this task i am using panda as my main libary and some other libaries for time series analysis.

The implementation is realized trough pandas and numpy.

Lars Penili
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Pandas supports merge, join, and concatenate methods to combine two or more dataframes into one. However in case of multivariate time-series forecasting it is critically important to build your dataframe thoroughly through features preprocessing routine. In other words, these two dataframes you're going to join should have the same properties (dimensions, temporal resolution, etc.) and common DateTimeIndex. The operation of joining two different dataframes can be implemented as finding their DateTimeIndex intersection points

furious_bilbo
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