Which of the following is NOT a commonly used forecasting technique for time series data?Review LaterARIMASARIMAProphetLinear Regression

The question is asking which of the following is NOT a commonly used forecasting technique for time series data. The options are:

1. ARIMA
2. SARIMA
3. Prophet
4. Linear Regression

To answer this question, we need to understand what each of these techniques is.

1. ARIMA: AutoRegressive Integrated Moving Average (ARIMA) is a forecasting technique that projects the future values of a series based entirely on its own inertia. Its main application is in the area of short term forecasting requiring at least 40 historical data points.

2. SARIMA: Seasonal AutoRegressive Integrated Moving Average (SARIMA) is an extension of ARIMA that explicitly supports univariate time series data with a seasonal component. It adds three new hyperparameters to specify the autoregression (AR), differencing (I) and moving average (MA) for the seasonal component of the series, as well as an additional parameter for the period of the seasonality.

3. Prophet: Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.

4. Linear Regression: Linear regression is a statistical approach for modelling relationship between a dependent variable with a given set of independent variables.

Out of these four, Linear Regression is not a commonly used forecasting technique for time series data. While it can be used for prediction in time series data, it does not take into account the time-dependent structure and seasonality of the data, which is a key aspect of time series forecasting. Therefore, Linear Regression is the correct answer.