Transformer-Based Multivariate Time-Series Forecasting

This project implements a Transformer architecture using PyTorch to perform multivariate time-series forecasting on the Electricity Load Diagrams dataset (UCI, 2011–2014). The goal is to predict future energy consumption based on past patterns across multiple clients.

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Project Overview

Feature	Description
Data	Electricity usage from 370 clients (15-min intervals)
Model	Transformer (PyTorch, custom positional encoding)
Input Window	24 hours of hourly consumption data
Prediction Horizon	Next 6 hours for all 10 clients
Metrics	MAE, RMSE, Visual Plots
Training Time	~45 minutes on Colab GPU

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Project Structure

File	Description
load_data.py	Loads and parses the .txt dataset
preprocess_data.py	Resamples, normalizes, and builds sequences
model.py	Transformer model with positional encoding
train.py	Training loop and validation logic
evaluate.py	Evaluation metrics and forecast visualizations
best_transformer_model.pth	Trained model checkpoint
sample1.png sample2.png sample3.png	True vs predicted plots
requirements.txt	Dependencies
README.md	This file

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Sample Results

The following plots show true vs predicted consumption for one client (Feature 0) across a 6-hour forecast.

Sample 0	Sample 1	Sample 2

Final Evaluation Metrics:

• MAE: 0.0019
• RMSE: 0.0020

The model accurately captures patterns on real energy data, with very low error, even when trained on a subset of clients.

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How to Run

1. Clone the Repository

git clone https://github.com/your-username/transformer-time-series-forecasting.git
cd transformer-time-series-forecasting