LSTM Time Series Prediction This repository provides an LSTM (Long Short-Term Memory) model setup and a flexible data loading pipeline for time series prediction tasks. It includes data preprocessing, sequence generation, and model training, built using PyTorch/Keras (choose one). The design supports easy adaptation for custom datasets and integration into larger machine learning workflows. 🔧 Features ✅ Preprocessing for raw time series data ✅ Sequence generation for supervised learning ✅ LSTM model architecture setup ✅ Model training & evaluation ✅ Custom dataset integration ✅ PyTorch/Keras (configurable) 📁 Project Structure bash Copy Edit ├── data/ │ └── your_dataset.csv ├── src/ │ ├── data_loader.py # Preprocessing & sequence creation │ ├── model.py # LSTM model definition │ ├── train.py # Training loop │ └── utils.py # Helper functions ├── config.yaml # Configurations for model and training ├── requirements.txt └── README.md 🚀 Getting Started 1. Clone the repository bash Copy Edit git clone https://github.com/siammahfuz/lstm-code-setup-and-data-loading/.git cd lstm-time-series 2. Install dependencies bash Copy Edit pip install -r requirements.txt 3. Add your dataset Place your time series CSV file inside the data/ folder. Ensure it includes a numeric column to be predicted. ⚙️ Configuration Edit config.yaml to adjust model and training parameters: yaml Copy Edit sequence_length: 50 batch_size: 64 epochs: 20 learning_rate: 0.001 input_size: 1 hidden_size: 64 num_layers: 2 output_size: 1 🧠 Training the Model Run the training script: bash Copy Edit python src/train.py 📊 Example Use Case The model can be used for: Stock price prediction Energy consumption forecasting Weather time series modeling Any other univariate or multivariate time series tasks 📌 Customization Modify data_loader.py to change preprocessing or sequence logic. Adjust model.py to experiment with different architectures. Configure logging, validation, or checkpointing in train.py. Developed by Md Mahfuzur Rahman Siam Software Tester & Programmer 📧 ksiam3409@gmail.com 🌐 https://siammahfuz.github.io/ 🤝 Contributing Pull requests and issues are welcome. Feel free to suggest features or improvements. 📜 License This project is licensed under the MIT License.

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