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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
…These involve a specific edge case where ds are too close to the granularity received by the gluons packages; the idea is to dephase by a repeated (more than 80%) time value in order to normalize the whole index, while preserving the previously detected stationality (and of course, the data within). The forecasting is then done and the time index fixed. Another important addition is that whenever this funcionality is harnessed, the user receives notification through the console, so no blackboxing is done when shifting ds column to make it compatible with gluonts
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Creating clean PR to fix the gluonts object handling.