A Library for Advanced Deep Time Series Models.

Code examples in pyTorch and Tensorflow for CS230

PyTorch深度学习快速入门教程（绝对通俗易懂！）

✔️李沐 【动手学深度学习】课程学习笔记：使用pycharm编程，基于pytorch框架实现。

Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah

CNN+BiLSTM+Attention Multivariate Time Series Prediction implemented by Keras

TexTeller can convert image to latex formulas (image2latex, latex OCR) with higher accuracy and exhibits superior generalization ability, enabling it to cover most usage scenarios.

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

RevIN: Reversible Instance Normalization For Accurate Time-series Forecasting Against Distribution Shift

🔧 用于深度学习的模块

安全微伴自动刷课助手

Official implementation of the paper "FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective"

Open-Source Implementations of Large Time-Series Models

Official implementation of the paper "Frequency-domain MLPs are More Effective Learners in Time Series Forecasting"

Official implementation of the paper "FilterNet: Harnessing Frequency Filters for Time Series Forecasting"

PyTorch implementation of "Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective" (NeurIPS 2024)

Time Series Forecasting, Distribution Shift

NuwaTS: a Foundation Model Mending Every Incomplete Time Series

用深度学习思路与线性回归思路对黄金期货价格进行预测

a representation learning method that predicts the Fourier transform of state sequences to improve sample efficiency of RL algorithms.

IN5000 TU Delft - MSc Computer Science - Data Analysis