EMD-VMD-TCN short-term load forecasting

PyTorch implementation of AirFormer, AAAI-23

Code for our Information Fusion paper MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction

局域均值分解的python实现

CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.

The code used in the master thesis ___

Forecasting Apple Inc stock prices using hybrid method of EMD (Empirical Mode Decomposition) and various machine learning algorithms such as ARIMA, RNN, Kernel-SVM, Gaussian Process and LSTM

Code for: A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso

Python version of the Multivariate Empirical Mode Decomposition algorithm

Air Quality Predictions with a Semi-Supervised Bidirectional LSTM Neural Network

Short-term Air Quality Prediction Based on EMD-Transformer-BiLSTM

In this project we are comparing various regression models to find which model works better for predicting the AQI (Air Quality Index).

Bayesian Change-Point Detection and Time Series Decomposition

StockFormer: A Swing Trading Strategy Based on STL Decomposition and Self-Attention Networks

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)

An open-source Python package to easily obtain real-time, historical, or forecasted air quality data for anywhere in the world. Reliable, accurate and simple.

Runner-up team (2nd place) in AI4VN2022: Air Quality Forcasting Challenge