Introduction
ARIMA, short for “AutoRegressive Integrated Moving Average,” is a statistical model used for time series forecasting. ARIMA is a powerful tool for analyzing time series data because it can capture both the short-term and long-term patterns in the data, as well as any trend or seasonality. It also captures both linear and non-linear relationships in the data, making it a powerful tool for modeling complex time series. The model is also relatively easy to implement.
ARIMA models are based on the idea of decomposing a time series into three components: autoregression (AR), integration (I), and moving average (MA). By combining these three components, an ARIMA model can accurately forecast the future values of a time series.
ARIMA is particularly useful for analyzing and forecasting stationary time series data (where the mean and variance of the data do not change over time) that exhibit patterns such as trend and seasonality. For example, it can be used to forecast sales, stock prices, weather patterns, and many other types of time series data. It can also be used in conjunction with other models, such as exponential smoothing, to improve the accuracy of forecasts.
This article will cover the following topics:
1. Background and Fundamentals
2. Components of ARIMA
3. Step By Step Implementation
4. ARIMA Advanced Application
5. Summary and Take away
