Forecast Techniques

Different Forecast Techniques

Though there are several techniques used by professionals, by default we use the following techniques and also have the facility to define your own custom techniques and use them.

1. Exponential Smoothing Forecast

   Simple exponential smoothing is one of the simplest ways to forecast a time series. The basic idea of this model is to assume that the future will be more or less the same as the (recent) past.

2. Auto Exponential Forecast

   Auto exponential smoothing is used to calculate optimal parameters of a set of smoothing functions in Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing.

3. Arima Forecast

   ARIMA (Auto Regressive Integrated Moving Average) model is a class of statistical models for analyzing and forecasting time series data. It explicitly caters to a suite of standard structures in time series data, and as such provides a simple yet powerful method for making skilful time series forecasts.

4. Auto Arima Forecast

   This Technique of Forecast uses an Automated version of the ARIMA Forecast Technique.

5. Auto Regression Forecast

   In a multiple regression model, we forecast the variable of interest using a linear combination of predictors. In an autoregression model, we forecast the variable of interest using a linear combination of past values of the variable.

6. Simple Moving Average

   A simple moving average (SMA) is an arithmetic moving average calculated by adding recent prices and then dividing that figure by the number of time periods in the calculation average.

7. Croston Forecast

   Croston Forecast Techniques used for Intermittent Demands. The Croston model answers the question "How much demand will we have on average per period?"

8. Seasonal Trend Forecast

   This Forecasting Technique considers variations which occur at certain regular intervals either on a weekly basis, monthly basis, or even quarterly (but never up to a year). Various factors may cause seasonality - like a vacation, weather, and holidays.

9. BATS Forecast

   BATS (Box-Cox transformation, ARMA errors, Trend and Seasonal components) is a Time Series Forecasting Method that is capable of modelling Time Series with Multiple Seasonalities.

10. TBATS Forecast

    TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components) is a forecasting method to model time series data. The main aim of this is to forecast time series with complex seasonal patterns using exponential smoothing.

11. Snaive Forecast

    Snaive forecasting is the technique in which the last period's sales are used for the next period's forecast without predictions or adjusting the factors.

12. Vector Regression

    Vector Autoregression is one of the multivariate forecasting algorithms. It uses two or more time series to influence each other. In this model, each attribute is a linear combination of the past values of itself and the past values of all the other variables.

13. Attach Rate

    Attach Rate is often expressed as a sales ratio of primary to secondary units, or as secondary units sold as a percent of primary. The secondary good/service may be an integral component of the primary purchase (e.g., Bluetooth circuits/capabilities sold within electronic devices) or it may require a further consumer decision (e.g., as in video games sold per unit of the primary console sold).

14. Best Fit

    Best fit forecasting is a procedure within most supply chain forecasting applications. It is a procedure that: Compares all of the forecasting models within that application for each item being forecast.

15. Dynamic Regression

    In dynamic regression, the conditional expected value of the response variable may also change slowly, or progressively, when the value of the explanatory variable changes.

16. Tslm Regression

    TSLM Regression is used to fit linear models to time series including trend and seasonality components.