Causal leading indicators detection for demand forecasting

By | July 24, 2015

Y.R. Sagaert, N. Kourentzes, E-H. Aghezzaf, B. Desmet, 2015, 27th European Conference on operational Research, Glasgow.

Demand forecasts are often univariate, or include only limited causal promotional information on a short-term horizon, which do not capture changing long-term global markets. More especially, including causal exogenous information in the forecasting models could enrich the long-term forecast. The limited historical data is typically used to both identify the current univariate structure and select the appropriate causal leading indicators from a large set of exogenous variables. A key challenge is to be able to distinguish between correlated and causal variables. The resulting variable selection problem is well studied in literature, but far from resolved. Furthermore, the problem gets harder by the limited available historical data in this context of business forecasting. The amount of historical sales observations is far less than the size of the pool of potential causal leading indicators. Methodologies from heuristics to shrinkage estimators, such as LASSO, are examined to overcome the variable selection problem. In a case study, we use real demand data from a global manufacturer and potential causal leading macro-economic indicators from the different global markets the manufacturer trades in.

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