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Time series analysis is a popular machine learning technique for forecasting trends of time-dependent variables such as stock price, GDP, and quarterly sales. Given the popularity (https://github.com/…
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Hi
Im trying to run deep learning optimisation using optuna. It works fine if I have n_trails=1 however if I increase that number to say 2 I get a error AttributeError: _model_call. I have enough c…
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- PyTorch-Forecasting version: 0.10.3
- PyTorch version: 1.13.1
- Python version: 3.8.16
- Operating System: Linux 5.10.147
### Expected behavior
I'm evaluating with DeepAR following the examp…
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Can the TFT model deal with missing time data?
I have some raw data that losses some timestamps. So the datetime is not always continuous. Can the TFT in pytorch-forecasting handles automatically or …
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Can you provide examples on timeseries forecasting ?
It is mentioned in the todo list of this repository.
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Hi,
I've noticed that some of the generated features exhibit look-ahead bias, which is critical and must be avoided in machine learning regression problems. Specifically, the features in X_train co…
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Overview: currently the GFS model which is used as inputs has a few datapoints which cover snow specific data: such as snowdepth and SWE as well as a couple of accumulation variables (accumulated prec…
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- PyTorch-Forecasting version: 0.8.3
- PyTorch version: 1.7.1
- Python version: 3.7.6
- Operating System: Ubuntu 18.04
### Similarities
I notice this is similar to #103 and #215, which were se…
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**Is your feature request related to a current problem? Please describe.**
I need to be able to robustly backtest any ForecastingModel so I can he a valid view how the model performs over time. As th…
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Currently using a so-so correlation setup via XGBRegressor on time-lagged field-entries, 5 day windows. It's pretty bad theory, but works decent in practice. We'll want to move to something more solid…