ARIMA - Simple Time Series Forecasting

<p>The <strong>ARIMA</strong> (AutoRegressive Integrated Moving Average) model is a powerful statistical method used for analyzing and forecasting time series data. It handles standard patterns in time series and offers a simple yet effective approach to forecasting.</p><p>ARIMA combines three components:</p><ul><li><p><strong>AR (Autoregression):</strong> Uses past values to predict future ones.</p></li><li><p><strong>I (Integrated):</strong> Applies differencing to make the data stationary by removing trends.</p></li><li><p><strong>MA (Moving Average):</strong> Models the relationship between observations and past forecast errors.</p></li></ul><p>The model is typically expressed as <strong>ARIMA(p, d, q)</strong> where:</p><ul><li><p>p is the number of lag observations,</p></li><li><p>d is the degree of differencing,</p></li><li><p>q is the order of the moving average.</p></li></ul><p>ARIMA builds a linear regression model using these components. Setting any parameter to zero simplifies the model to AR, MA, or ARMA. The model assumes the data follows an ARIMA process, making it essential to check whether the data and residuals meet this assumption.</p><p></p><p>This workflow demonstrates the simple use of ARIMA for forecasting on time series data. This workflow includes:</p><ul><li><p>data preparation (data import and preproccessing)</p></li><li><p>data analysis (stationary and auto correlation test)</p></li><li><p>build the model</p></li><li><p>evaluate the model</p></li><li><p>visualization</p></li></ul><p>The simple dataset used consists of only 2 columns, namely <strong>dates</strong> and <strong>y</strong> values.</p>