Deep neural network models implemented from scratch in PyTorch for time series forecasting.
This repository contains implementations of various deep learning models for time series forecasting, all built from scratch using PyTorch. The models included are:
- Vanilla Recurrent Neural Network (RNN)
- Gated Recurrent Unit (GRU)
- Long Short-Term Memory (LSTM)
- Transformer
These implementations are designed to help understand the inner workings of these models and to provide a solid foundation for building more complex time series forecasting solutions.
- Vanilla RNN: A basic recurrent neural network that captures temporal dependencies in sequential data.
- LSTM: Advanced RNN variant designed to remember/forget past temporal dependencies.
- GRU: Simplified version of LSTM that offers similar peformance with fewer gates.
- Transformer: A state-of-the-art model using attention mechanisms. Decoder based transformer is implemented for generative applications.
A classic time series dataset that records the monthly totals of international airline passengers from 1949 to 1960. The dataset is included in the repository (airline-passengers.csv).
The dataset contains the following columns:
Month: The month of the observation.Passengers: The number of passengers carried by the airline.
A collection of data related to electricity consumption in Panama, specifically aimed at forecasting future electricity loads. This dataset typically includes historical records of electricity consumption, possibly broken down by region or time intervals (like hourly, daily, or monthly). It may also incorporate additional variables such as weather parameters (temperature, humidity, precipitation) and special days (public holidays, weekends) that could influence electricity demand.
This is a multivariate dataset with many columns. For more info: Kaggle dataset page
This project is licensed under the MIT License - please see the LICENSE file for details.