Awesome Wireless Traffic Forecasting Library

Intoroduction

• This is an open-source library for wireless traffic forecasting (WTF-Lib), especially for deep wireless traffic analysis.
• In this library, we provide a variety of state-of-the-art short- and long-term wireless traffic forecasting methods.
• We provide a neat code base to evaluate advanced deep time series models or develop your model, which given a strong benchmark.

Leaderboard for WTF

Till November 2023, the top three models for short- and long-term WTF tasks are:

| Model

Ranking | Long-term

Forecasting | Short-term

Forecasting | Runtime | | ---------------- |---------------------------------------------------| ------------------------------------------------------------ |----------------------------------------------------| | 🥇 1st | PSLinear | PSLinear | DLinear | | 🥈 2nd | PatchTST | PatchTST | PSLinear | | 🥉 3rd | GWNet | FEDformer | STID |

Compared models of this leaderboard. ☑ means that their codes have already been included in this repo.

• [x] PSLinear - Progressively Supervision based on Label Decomposition: Towards Long-Term Wireless Traffic Forecasting on Large-Scale Graphs. arXiv 2025, Code.

• [x] PatchTST - A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. [ICLR 2023] [Code].

• [x] DLinear - Are Transformers Effective for Time Series Forecasting? [AAAI 2023] [Code].

• [x] GWNet - Graph WaveNet for Deep Spatial-Temporal Graph Modeling. [arXiv 2019][Code].

• [x] STID - Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting. [arXiv 2023][Code].

• [x] DyDgcrn - Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution. [[IEEE TKDE 2021]] (https://ieeexplore.ieee.org/document/9625773) [Code].

• [x] Mvstgn - MVSTGN: A Multi-View Spatial-Temporal Graph Network for Cellular Traffic Prediction. [[IEEE TMC 2021]] (https://ieeexplore.ieee.org/document/9625773) [Code].

• [x] Periodformer - Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs? [[arXiv 2023]] (https://arxiv.org/abs/2306.05035) [Code].

• [x] FEDformer - FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. [ICML 2022] [Code].

• [x] Pyraformer - Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting. [ICLR 2022] [Code].

• [x] Autoformer - Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. [NeurIPS 2021] [Code].

• [x] Informer - Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. [AAAI 2021] [Code].

• [x] Reformer - Reformer: The Efficient Transformer. [ICLR 2020] [Code].

• [x] STCNet - Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data. [IEEE JSAC 2019] [Code]

• [x] Transformer - Attention is All You Need. [NeurIPS 2017] [Code].

Long-term Wireless Traffic Forecasting (LWTF) Benchmark