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Could you add some notes to these models, especially the corresponding paper links? For example, in this paper, Enhancing the locality and breaking the memory bottleneck of transformer on time series …
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**Description:**
I am proposing an enhancement for the `Forecaster` class in the `functime` library, specifically to support setting `lags=0` in classes that inherit from `Forecaster`, such as `lin…
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Really cool project! Enjoy the paper and have had fun testing it out. Will instructions on fine tuning be released?
Thanks for your time
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These will need a read for comparison
Owens 2008 Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics‐based schemes with 8 years of L1 observations: https://agupubs.…
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Hi Gregor, many thanks for an amazing package and all the great work!
Is there a way to use the models we estimate for out-of-sample forecasting (maybe some of the underlying filtering packages sup…
nflp updated
11 months ago
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As an AIFES researcher I want to be able to have a testbed setup which allows me to easily and quickly iterate over different forecasting pipelines*, so I can compare many forecasting pipelines* and d…
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The Nixtla folks claim that their `statsforecast.AutoARIMA` is faster than `pmdarima` and `prophet`:
![image](https://user-images.githubusercontent.com/316517/228058200-89b9498f-0bcc-40c3-bd8a-51f4…
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- PyTorch-Forecasting version: 0.9.0
- PyTorch version: 1.9.0+cu102
- Python version: 3.7.4
- Operating System: Windows 10 Pro
### Expected behavior
I have a dataframe of over 2000 items, and…
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Hello,
As we talked before about adding Spatio-Temporal GNN models to PyG. I suggest papers that I mentioned below for start. Please take a look at them.
1-[Structured Sequence Modeling with Gr…