ARIMA model means Autoregressive Integrated Moving Average. This model provides a family of functions which are a very powerful and flexible to perform any task related to Time Series Forecasting. In Machine Learning ARIMA model is generally a class of statistical models that give outputs which are linearly dependent on their previous values in the combination of stochastic factors.

While choosing an appropriate time series forecasting model, we need to visualize the data to analyse the trends, seasonalities, and cycles. When seasonality is a very strong feature of the time series we need to consider a model such as seasonal ARIMA (SARIMA).

The ARIMA model works by using a distributed lag model in which algorithms are used to predict the future based on the lagged values. In this article, I will show you how to use an ARIMA model by using a very practical example in Machine Learning which is Anomaly Detection.

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