Time-Series-Forecasting-of-Electric-Production

Project Overview

This project aims to forecast the monthly electric production in the United States using time series analysis techniques. The dataset contains historical data from January 1985 to December 2018, and the objective is to build a predictive model that can be used by policymakers and energy companies for planning and decision-making.

Dataset

Link: Electric Production Dataset

Date Range: January 1985 - December 2018 Features: Date: Observation date Production: Electric production in millions of megawatt hours

Methods Used

Statistical Tests:

Mann-Kendall Trend Test Kruskal-Wallis Test

Time Series Models:

Holt-Winters Exponential Smoothing ARIMA (AutoRegressive Integrated Moving Average) SARIMA (Seasonal ARIMA)

Model Evaluation Metrics:

Root Mean Squared Error (RMSE) Mean Absolute Percentage Error (MAPE)

Results

The ARIMA model provided the best performance based on RMSE and MAPE, making it suitable for forecasting future electric production.

Future Work

Implementing other advanced time series models like Prophet. Extending the model to include exogenous variables like temperature, economic factors, etc.