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This tutorial contains an example with multiple indexes and subsequent tampering to the data in order to show resiliency and a real life use case of TC applied on cryptocurrency prices up to 2021.
Fix to the issue raised on empty dataframe, resulting from an inner merge where the existing dataframe that accumulated results and the newer one had diferent indices. A subsequent issue must be raised to either: 1 report a single model failure (on index matching) 2 fix the moirai discrepancy (only model that showed this issue)
… the title suggested
…for nfl bets. Processor will take the mode of the last 3 days to select a spread consensus, spread consensus prices will later be fetched accross all data in order to have the most complete timeseries. Final result of processor file execution is a csv that contains timestamps, spread consensus information, and who's playing who. Y column will contain odds for that specific ratio. This file is compatible with timecopilot and interpretation of the final forecast will be determined by the spread registered in the id's and the final price forecasted on markets by timecopilot. This can be an average for the next day or high/low. I leave that choice for the crew's choosing.
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Simple pull request as a proposal in order to add and allocate the betting process.
This one contains nothing but the proposed extractor file that'll ping the betting api and extract data in order to update a json file to be analyzed later on.