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The proposal is to add recursive feature elimination algorithm for optimizing forecasting models
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I am running the code for the paper titled as "Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations" meet a question. There is an error reported—— ValueErr…
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- PyTorch-Forecasting version: 0.8.4
- PyTorch version: 1.8.1
- Python version: 3.8
- Operating System: Ubuntu 20.04.2 LTS
### Expected behavior
In order to generate the interpretation plots …
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Another interesting-looking paper!
https://arxiv.org/abs/2202.11214
To quote the abstract:
> FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather foreca…
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Now the unit tests for the time series forecasting are quite formal and the low-quality predictions can still be fine in the test (because the benchmarks are too simple or the pass criteria are too we…
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Hello there, there are a few other approaches to this that I have seen and wondered if they are on your radar.
Bellman Conformal Inference (BCI) - optimises prediction intervals for time series …
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This issue serves as an umbrella issue for integrating networks from LTSF-Linear. Deep learning has proven to be an effective way to predict time series data. To expand this type of forecasting in skt…
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Hi @aa25desh and welcome to MLJ.jl!
Here are some time series forecasting features I find very valuable:
Check out @robjhyndman's free book on forecasting: https://otexts.com/fpp2/
**Univariate …
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Hi,
I have noticed that the CPU utilization when using deployed MOJO models for forecasting is low (in my case, I am using a DRF model and CPU utilization is below 10%). Is there a way to speed up …
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### Overview
The objective of this project is to predict the volatility of Yahoo's stock prices using historical data. The dataset contains various features including opening price, closing price, ad…