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.
File Format
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 |
Required Parameters
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.
Example Notebook
You can find an end-to-end example at the link below.
https://www.kaggle.com/isaacmg/time-series-classification-with-flow-forecast-ff
Design and Implementation
Check List
- Implement and test GeneralClassification Data Loader
- Add calculation of additional metrics
- Precision
- F1
- Revised plotting to Weights and Biases
- Logging of ROC plots
- Refactoring and cleaning up plot functions in trainer.py
- End to end integration test
- Example classification Kaggle notebook
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 case…
What other functions can