How to forecast data based on variables from different datasets?
You can use pandas.DataFrame to create 4 dataframes for each dataset, then proceed with combining them in one dataframe by using merge
For a university project I’m trying to see the relation oil production/consumption and crude oil price have on certain oil stocks, and I’m a bit confused about how to sort this data.
I basically have 4 datasets-
-Oil production
-Oil consumption
-Crude oil price
-Historical price of certain oil company stock
If I am trying to find a way these 4 tables relate, what is the recommended way of organizing the data? Should I manually combine all this data to a single Excel sheet (seems like the most straight-forward way) or is there a more efficient way to go about this.
I am brand new to PyTorch and data, so I apologise if this is a very basic question. Also, the data can basically get infinitely larger, by adding data from additional countries, other stock indexes, etc. So is there a way I can organize the data so it’s easy to add additional related data?
Finally, I have the month-to-month values for certain data (eg: oil production), and day-to-day values for other data (eg: oil price). What is the best way I can adjust the data to make up for this discrepancy?
Thanks in advance!