Time series analysis is a powerful tool that can be used to predict future events. It is a form of statistical analysis that uses past data points to build a model that can be used to forecast future events.
Time series analysis can be used for a variety of purposes, such as:
There are many different methods that can be used for time series analysis. The most common methods are:
Each of these methods has its own strengths and weaknesses. The choice of which method to use depends on the specific problem that you are trying to solve.
In this post, we will focus on the SARIMA model. SARIMA stands for Seasonal Autoregressive Integrated Moving Average. It is a type of ARIMA model that is used when there is seasonality in the data.
SARIMA models are used to predict future values of a time series based on past values. The model takes into account the seasonality of the data when making predictions.
The SARIMA model is made up of three components:
The AR component is used to model the autocorrelation in the data. The I component is used to model the non-stationarity in the data. The MA component is used to model the moving average of the data.
The SARIMA model is fit to data using the maximum likelihood estimation (MLE) method. MLE is a method of estimating the parameters of a statistical model.
Once the SARIMA model is fit to the data, it can be used to make predictions about future values of the time series.
There are a few things that you need to keep in mind when using SARIMA models:
If the data is not stationary, it can be made stationary by differencing. Differencing is a process of subtracting the previous value from the current value.
If the data is not seasonally stationary, it can be made seasonally stationary by seasonal differencing. Seasonal differencing is a process of subtracting the previous value from the current value, but only for values that are a certain number of time periods apart.
Once the data is stationary, the SARIMA model can be fit to the data. The SARIMA model has three parameters:
The SARIMA model is fit to the data by finding the values of p, d, and q that minimize the error between the predicted values and the actual values.
Once the SARIMA model is fit to the data, it can be used to make predictions about future values of the time series. The predictions made by the SARIMA model are based on the past values of the time series.
The SARIMA model is a powerful tool that can be used to predict future values of a time series. However, it is important to remember that the predictions made by the SARIMA model are only as accurate as the data that is used to fit the model.