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This code is a PyTorch implementation of our ICLR'24 paper "Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting". [arXiv](https://arxiv.org/abs/2402.05956)
TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and anomaly detection. It functions as a pretrained model for predicting future values in univariate time series data, eliminating the need for manual training from scratch. The project includes a framework for adapting pretrained weights to specific datasets using low-rank adaptation to improve accuracy. It also provides specialized capabilities for integrating time-series predictions as tools within autonomous AI agent architectures and automated workflows. The system supports
This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque
The repo is the official implementation for the paper: iTransformer: Inverted Transformers Are Effective for Time Series Forecasting. [[Slides]](https://cloud.tsinghua.edu.cn/f/175ff98f7e2d44fbbe8e/), [[Poster]](https://cloud.tsinghua.edu.cn/f/36a2ae6c132d44c0bd8c/), [[Intro…
The main features of thuml/itransformer are: Forecasting Models, Time Series Forecasting Models.
Projects with overlapping indexed features include: decisionintelligence/pathformer — This code is a PyTorch implementation of our ICLR'24 paper "Pathformer: Multi-scale Transformers with Adaptive… wxie9/card. google-research/timesfm — TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and… princewen/tensorflow_practice — This repository is a collection of practical deep learning implementations and examples built using the TensorFlow… lyhue1991/eat_tensorflow2_in_30_days — This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow.… adityalab/lstprompt — Implementation of the paper "LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term…
