continued from Part-1 of Models for DM module ...
6. Combined Transformation Model(elog): Works in both DP & PE mode of analytical engine run and is combination of Transformation & CMReg model. This model performs CMReg operations on log transformed time series.
7. Multiplicative Monte Carlo Regression(CMReg): This model comes into picture for both DP &PE mode run as well, and utilizes long Causal Factors.
8. Integrated Causal Exponential Model(BWint): This also is a regression model, known as Multiplicative regression - winter model. The model works only in DP mode. It runs multiplicative regression on Causal data (short CFs) and then smooths out the residual series exponentially like HOLT. It helps modeling Trends, Seasonality and Causality in demand data.
9. Auto & Linear Regression: It is available in DP mode run and includes auto-regression. Causal factors which are used by this model are Constant & events.
NOTE: All the 15 models can be used by engine in DP mode run, while in PE mode following models are not available:
- ARIX, ARLOGISTIC, ARX, BWINT, FCROST, ICMERGR & IREGR
2 comments:
Hi,
I find the content to be very useful? Can you plz give some insight into tuning Model forecast quantities? I mean what settings one need to tweak to minimize the forecast error?
regards--sasi
Hi Sasidhar,
thanks for the comments.
Forecast error depends on too many factors, various parameters defined at system level and at model levels.
One common way of improving forecast accuracy is by, incorporating related Causal Factors. Also nodal tuning is suggested for combinations which are way off, when it comes to forecast numbers.
I would be writing very soon about Engine in next few blog entries.
Rgds
Raj
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