The model is a standard transformer modified to take in time series data where a fully connected layer is added before the input of the endocer.
The first article explains step by step how to code the Transformer model used in the paper "Deep Transformer Models for Time Series Forecasting: The Influenza ...
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case, in arXiv 2020. [paper]; Adversarial sparse transformer for time series ...
Jan 23, 2020 · In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data.
This repository contains two Pytorch models for transformer-based time series prediction. Note that this is just a proof of concept and most likely not bug ...
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This repo included a collection of models (transformers, attention models, GRUs) mainly focuses on the progress of time series forecasting using deep learning.
Abstract: This repository contains the experimental work developed that has explored the usage of Transformer models for time-series forecasting. In particular, ...
TSlib is an open-source library for deep learning researchers, especially for deep time series analysis. We provide a neat code base to evaluate advanced deep ...
The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.
TFTS (TensorFlow Time Series) is an easy-to-use time series package, supporting the classical and latest deep learning methods in TensorFlow or Keras.
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