Time Series Analytics using R programming to predict energy consumption in a household This project is based on a data set obtained from Kaggle, and written in R programming language.
Introduction: We are going to predict household energy consumption for the period of over 2 years. The data set is of energy consumption collected from the author’s apartment unit for over two years. The objective of this project is to use time series analysis and forecasting methods to make predictions about future energy consumption patterns for that household. Time series analysis and forecasting methods are widely used in various fields, including economics, finance, and engineering, to make predictions based on historical data.
The project will use R programming language and its packages, such as forecast, and zoo, to perform time series analysis and forecasting. The following steps will be followed:
Data Preprocessing: The data will be cleaned and preprocessed to remove missing values, outliers, and any other anomalies.
Exploratory Data Analysis: The data will be visualized to gain insights into patterns, trends, and seasonality.
Time Series Modeling: The data will be modeled using different time series models such as ARIMA, to identify the best model for forecasting.
Naive forecast Tailing MA Two-level forecasting ARIMA Holt’s - Winter model Model Evaluation: The performance of the selected model will be evaluated using various metrics such as mean squared error (MSE) and mean absolute error (MAE).
Forecasting: The selected model will be used to forecast future energy consumption patterns for the household.