Probabilistic Forecasting in Darts
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We want a prediction that doesn’t just provide us the value that, according to the model, is most likely going to be close to the ground truth, but we also want a measure for how certain we can be about that value. To do this we will use one of Darts’ deep learning models, the TCN, in its probabilistic form.
A trained probabilistic model, rather than a deterministic function mapping input to output, can be seen as a mapping of an input series to a random variable.
The idea of how probabilistic TorchForecastingModel instances in Darts differ from their deterministic counterparts, is that instead of predicting future values directly, they predict parameters of a given distribution, which should describe the likelihood of seeing a future value.