Deep learning PyTorch library for time series forecasting
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Updated
Aug 19, 2023 - Python
Deep learning PyTorch library for time series forecasting
Time series Forecasting on the NTU_RGB+D skeleton dataset using AutoFormer and FEDFormer
PyTorch image classification models pre-trained
Training a financial forecasting model based on the AutoFormer architecture with PyTorch and Alpaca.py
An autoregressive forecasting implementation of a LSTM network, NBEATS architecture, ARIMA and SARIMAX regressions, and Autoformer architecture on rupee dollar exchange rates using pytorch, pytorch lightning, pytorch-forecasting, and GluonTS
This study examines the effectiveness of transformer-based models for financial time series forecasting, specifically focusing on log returns derived from daily closing prices of the DAX40 index. We propose a decoder-only transformer model designed for immediate-term financial time series forecasting: The PatternDecoder.
Tokenization Matters: A Fair Ablation of Point-wise, and Variate-wise Transformers for Financial Time Series. (includes PatchTST, iTransformer, Crossformer, Autoformer, Fedformer, Informer, TimeNet, and non-stationarity extensions.
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