Question

Best Forecasting Model

Hi, I am trying to find the best suitable model for my project that I should use for forecasting the values. Following are the models that I used and their respected values.

Model	RMSE	MAE	MAPE
Holt's	201.616	130.072	2.054
Holt's (Log Transform)	197.723	125.330	1.971

Damped Trent .

206.040

128.525

2.028

Answer 1

TRY ONE OF THESE TWO APPROACH, THIS MAY HELP:

Autoregressive Integrated Moving Average (ARIMA): –

A statistical technique that uses time series data to predict future. The parameters used in the ARIMA is (P, d, q) which refers to the autoregressive, integrated and moving average parts of the data set, respectively. ARIMA modeling will take care of trends, seasonality, cycles, errors and non-stationary aspects of a data set when making forecasts.

Understanding ARIMA Model in General Term: –

How to Understand ARIMA model?

To understand this, we can refer real time scenario that is sugar cane juicer, from juicer it is difficult to extract all the juice in one go, so the shopkeeper repeats the process for several times till there is no more juice left in the residual. That’s how ARIMA works, the idea with ARIMA models is that the final residual should look like white noise otherwise there is juice or information available in the data to extract.

How Do We Use ARIMA Model?

ARIMA checks stationarity availability in the data, the data should also show a constant variance in its fluctuations over time. To get the proper information about the parameter used in ARIMA is based on “identification process” which was purposed by Box-Jenkins.

When Do We Use ARIMA Model?

As we all know ARIMA is mainly used to project future values using historical time series data. Its main application is in short forecasting with minimum 38-40 historical data points with minimum number of outliers. If you do not have at least 38 data points, then it advisable to look for some other methods.

OR

CROSTON:

It’s modification of exponential smoothing for sporadic demand product time series suggested by Croston in 1972. The core value of this method is not only the estimation of average demand volume, but also estimation of time interval length between two non-zero demands, term called as intermittent demand.

The Croston method works in two steps, First, separate exponential smoothing estimates are made of the average size of a demand. Second, the intermittent demands are calculated. This is then used in a form of the constant model to predict the future demand.

How Croston’s Work?

Croston’s has a complex formula, however, what it output is actually very simple. The screenshot below explains what Croston’s does in a very simple way for the sake of understanding.

Above is the 12-month average vs. Croston’s vs, while below is the 5-month average vs. Croston’s.

As you can see, Croston’s removes the periods that have no demand only averaging the periods that have demand. Next Croston’s calculates the frequency of the demand. The math behind this is complex, but the output is extremely similar to performing an exponential smoothing.

Why Do We Use CROSTON’s?

In the given fig. we have two Croston’s forecast based on demand histories, with more non-zero data-points. Here Croston’s will come into the picture and show its benefits.

• At the very beginning, Croston’s starts detecting cyclic and periodicity into the data points of demand patterns. In this case, it is suggested that a demand could occur possibly after 3.5 (4 after roundup) zero period.

• The second most important thing which Croston’s does is, it adjusts the next occurrence from the last non-zero period, if the recent periods are zero periods.

So the objects of forecast are predicting the consumption at the right moment with right quantity. Croston’s does try to predict the “right moment”, which is more sophisticated than the moving average.

Conclusion:

The Croston method is a forecast strategy for products with intermittent demand. In the univariate forecast profile, choose forecast strategy.

Croston’s can be easily emulated with exponential smoothing and any timing benefit is usually adjusted by order lot sizing, and or safety stock in supply planning. Therefore, demand history must not only be lumpy, but must also be very low in order for Croston’s to be of value. Therefore, Croston’s can be seen as a specialty forecasting method that provides value in certain limited circumstance.