rasnarim / timeseriesforecastingstudydenmarkenergy

"Time Series Forecasting Study: Denmark Energy" - A Jupyter notebook analyzing Denmark's energy consumption using ExponentialSmoothing, AR, ARIMA, and SARIMA models. It includes data wrangling, EDA, and model evaluation, offering insights into energy trend predictions.

Time Series Forecasting Study: Denmark Energy

Introduction

This repository contains a Jupyter notebook that focuses on forecasting energy consumption in Denmark using time series analysis. The study employs models like ExponentialSmoothing, Autoregression (AR), ARIMA, and SARIMA with exogenous parameters. It aims to provide an in-depth analysis of energy trends and forecasting accuracy.

Requirements

To run this notebook, you'll need:

• Python 3.x
• Jupyter Notebook
• Libraries: pandas, numpy, statsmodels, matplotlib, seaborn

Installation

Clone the repository to your local machine:

Usage

To view and run the notebook:

1. Navigate to the cloned directory.
2. Start Jupyter Notebook: Open TimeSeriesForecastingStudyDenmarkEnergy.ipynb.

Contributing

Contributions to this project are welcome! Please fork the repository and submit a pull request with your changes.

Contact

For any queries, please reach out to [email protected].