timeseries_forecasting_for_weather

Introduction

This document provides an overview of a time series forecasting model for weather prediction using a Long Short-Term Memory (LSTM) neural network. The model is implemented in TensorFlow Keras and uses data preprocessing and training procedures to predict future weather conditions.

Architecture

The model employs a Long Short-Term Memory (LSTM) architecture, which is well-suited for time series forecasting due to its ability to capture long-term dependencies. The model is designed to process resampled weather data, forecast future temperature, and operates on normalized input features.

Training

The training data is sourced from the Jena Climate dataset, which includes 300,000 data points recorded every 10 minutes from January 10, 2009, to December 31, 2016. The data is resampled to hourly intervals, resulting in 720 observations used for training and predicting temperature 12 hours ahead. The model utilizes 71.5% of the dataset for training, with features normalized to a range of [0, 1]. Key hyperparameters include a learning rate of 0.001 and the Adam optimizer with default settings.

Guide: Running Locally

1. Setup Environment:

   • Ensure Python and TensorFlow are installed.
   • Install necessary libraries: pip install tensorflow keras.

2. Data Preparation:

   • Download the Jena Climate dataset.
   • Resample and normalize the data using the provided preprocessing script.

3. Model Training:

   • Use the script to define and compile the LSTM model.
   • Train the model using the prepared dataset.

4. Evaluation:

   • Evaluate the model's performance on a test dataset.

For enhanced performance, consider using cloud GPUs such as those offered by Google Cloud Platform, AWS, or Azure.

License

The project is released under the Apache 2.0 License, allowing for open-source usage and distribution with proper attribution.