Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data

Time Series Prediction with LSTM Using PyTorch

(pytorch)time_series_data-prediction-with-gru-and-lstm

A hands-on project for forecasting time-series with PyTorch LSTMs. It creates realistic daily data (trend, seasonality, events, noise), prepares it with sliding windows, and trains an LSTM to make multi-step predictions. The project tracks errors with RMSE, MAE, MAPE and shows clear plots of training progress and forecast results.

Long Short Term Memory unit (LSTM) was typically created to overcome the limitations of a Recurrent neural network (RNN). The Typical long data sets of Time series can actually be a time-consuming process which could typically slow down the training time of RNN architecture. We could restrict the data volume but this a loss of information. And in any time-series data sets, there is a need to know the previous trends and the seasonality of data of the overall data set to make the right predictions.

LSTM-based Bitcoin price prediction using PyTorch. Explore deep learning for time-series forecasting with data preprocessing and model training. Ideal for cryptocurrency enthusiasts and those interested in financial forecasting.

End-to-end time series forecasting portfolio with 8 real-world applications. Implements LSTM, Transformer, CNN, Autoencoder architectures for fraud detection, sensor monitoring, activity recognition & energy prediction. Complete with preprocessing pipelines, model deployment & comprehensive READMEs. PyTorch/TensorFlow.

LSTM Time Series Prediction of the daily COVID-19 cases using PyTorch in Python.

LSTM; Pytorch; Time Series Prediction; Air Quality Prediction;

Time Series Analysis for Electric Production Prediction using LSTM in PyTorch

Covid-19’s death prediction in Indonesia using LSTM Time Series in Pytorch

This repository implements Long Short-Term Memory (LSTM) networks and their variants for time series forecasting and sentiment analysis. It uses PyTorch to implement LSTM, GRU, Bidirectional LSTM for time series prediction, and LSTM, 2-Layer LSTM, CNN + 2-Layer LSTM for classifying suicidal vs non-suicidal tweets.

Pytorch LSTM Time Series Prediction Framework

Time series prediction using LSTM implemented with Pytorch

Implementations of a deep multivariate lstm used for high-dimensional time series prediction in pytorch

📊 Forecast time-series data using LSTM models in PyTorch; generate, train, and visualize predictions with key metrics for accurate insights.

LSTM for time series prediction example

LSTM (PyTorch) , time series prediction: a benchmark case w/ and w/o stationarity + scaling

TensorFlow and PyTorch models. LSTM neural networks in order to make predictions on (time series) financial data are created with both libraries.

LSTM model setup and data loading pipeline for time series prediction. Includes preprocessing, sequence generation, and model training using PyTorch/Keras. Designed for flexibility with custom datasets and easy integration into larger ML workflows.

Glucose Level Forecasting — Built end-to-end time-series forecasting pipeline to predict 30-minute ahead glucose from CGM data using feature engineering, RandomForest and LSTM; evaluated with MAE/RMSE and visualized predictions. Tech: Python, Pandas, PyTorch.

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A demo for LSTM time series prediction in PyTorch with simple data

PyTorch LSTM time-series forecasting with AUV-aware preprocessing, tuning, and prediction exports.

A project that applies LSTM (PyTorch) and XGBoost to time series prediction of BNB cryptocurrency price.

AI stock prediction website template for LSTM machine learning(https://github.com/jinglescode/time-series-forecasting-pytorch)

Time Series prediction with LSTM recurrent neural network of deep learning of ML - built with Keras Tensorflow & Pytorch

LSTM-based financial time series forecasting with PyTorch — custom R² loss, lag feature engineering, and multi-target prediction on 22M+ samples

In this project, I have implemented a 4 hidden layer stacked LSTM RNN architecture to solve the univariate time series problem of google stock price prediction using Pytorch.