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Graph neural networks are a popular choice for forecasting hierarchically structured panels, graph structured panels, or structured variable settings, e.g., energy networks with time series observed a…
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### 1. Model description
Autoformer, Transformer and Fedformer should be included in PyPOTS as an forecasting model.
```
@article{wu2021autoformer,
title={Autoformer: Decomposition transformers …
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Use flight specification of commercial UAV used for agriculture and forecast suitability of flight conditions. Key parameters are:
wind speed, wind gusts, rain, temperature. A nice paper on this is …
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### Deep Learning Simplified Repository (Proposing new issue)
:red_circle: **Project Title** : Customer Retention Risk Prediction
:red_circle: **Aim** : Predict customer churn risk in the bankin…
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I would like to work on this in the respective manner:
Problem Description:
This model aims to address the challenge of forecasting future expenses by analyzing historical spending patterns, up…
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If innovative work is necessary, do the authors plan to add experiments on precipitation or radar extrapolation forecasting
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This list is a quick bucket list of items that can be potentially improved from what I've seen in the docs.
As requested by @yarnabrina, we should create simple examples, reducing complexity of th…
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# Food Demand Forecasting for Restaurants
**Tier:** 2-Advanced
Food demand forecasting for restaurants is an application built to estimate the food demand that a restaurant is expected to receiv…
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Each one of the members will implement a different ML predicting model.
Definition of Done
- [ ] The model accuracy is decent
- [ ] The implemented model is reviewed by peer
- [ ] The script for the …