Comments (6)
To me it is not very clear what you want to achieve here. The FFT performs a fourier transform which transform your time-series into a frequency representation which is not a timeserie anymore. Are you referring instead to a Short-time fourrier transform (https://en.wikipedia.org/wiki/Short-time_Fourier_transform) which take a sliding window and perform the fourrier transform on that?
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The idea is to approximate given time serie with FFT & extrapolate for future points.
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Gael take a look at https://www.youtube.com/watch?v=VYpAodcdFfA this is what we plan to implement and was suggested by Julien
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I see, you need to filter some of the frequency (e.g. low pass filter) in the frequency domain and use that to do forecasting. Otherwise you are just back with the same initial time-serie just repeating.
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Yes the forecasting algorithm relies on the Fourier Transform but it is not an implementation of FFT, I agree that maybe the name of the task is not detailed enough.
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Closing as it's implemented already.
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Related Issues (20)
- `TFTModel.predict()` with `predict_likelihood_parameters=True` HOT 2
- Update examples
- Shap explanation HOT 2
- TFTExplainer: getting explanations when trained on multiple time series HOT 1
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- [BUG] No activations in BlockRNNModel output MLP
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- TiDE Model Stops At A Specific Epoch HOT 1
- Backtest with custom forecast dates HOT 2
- [Question] window_agg parameter not clear in anomaly scorers HOT 1
- [BUG] Training on multiple gpus HOT 1
- [BUG] How to prevent darts from caring about the date frequency HOT 2
- Question of validation data length and how samples are extracted from the validation dataset HOT 2
- [QUESTION] Behavior of TFT's .predict() method HOT 2
- [BUG]When the time_col parameter in TimeSeries.from_group_dataframe contains integers, it is still converted to a DatetimeIndex HOT 1
- [QUESTION] Simple question regarding backtest() in Darts. HOT 2
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