Training time series classification models is a relatively new feature in FF. It follows the same general approach as forecasting models but with a few subtle differences. See our full tutorial notebook on Kaggle here.
For our GeneralClassificationLoader
class files should be pre-formated as follows. Future data-loaders may feature additional
Datetime | MeanMilkProduction (kg/lactation) | Temperature | cow_class |
---|---|---|---|
12/24/20 | 95 | 55 | |
12/25/20 | 190 | 45 | |
12/26/20 | 165 | 42 | |
12/27/20 | 8552 | ||
53 | 10529 | ||
54 | 9924 | 2 | |
92 | 10533 | ||
115 | 9167 | ||
180 | 9289 | ||
97 | 8343 | ||
84 | 9005 | ||
82 | 8867 | 4 | |
80 | 9352 | ||
77 | 8900 |
In general parameters are similar to the parameters you would supply models for TS forecasting
sequence_length
: The length of the time series sequence you wish to classify. At the moment GeneralClassificationLoader
solely supports sequences if the same length. If you have sequences of varying lengths we recommended padding the sequences.
You can find an end-to-end example at the link below.
https://www.kaggle.com/isaacmg/time-series-classification-with-flow-forecast-ff
Check List
Questions?
Can existing plot functions work with classification tasks?
Is a test-loader really necessary? Why would we want to plot the results in this caseā¦
There has been some odd behavior with the ROC curves so we may want to look into that 12/21/22