  The demand for a particular product or service is typically associated with
different uncertainties that can make them volatile and challenging to predict.
Demand unpredictability is one of the managers' concerns in the supply chain
that can cause large forecasting errors, issues in the upstream supply chain
and impose unnecessary costs. We investigate 843 real demand time series with
different values of coefficient of variations (CoV) where promotion causes
volatility over the entire demand series. In such a case, forecasting demand
for different CoV require different models to capture the underlying behavior
of demand series and pose significant challenges due to very different and
diverse demand behavior. We decompose demand into baseline and promotional
demand and propose a hybrid model to forecast demand. Our results indicate that
our proposed hybrid model generates robust and accurate forecast across series
with different levels of volatilities. We stress the necessity of decomposition
for volatile demand series. We also model demand series with a number of well
known statistical and machine learning (ML) models to investigate their
performance empirically. We found that ARIMA with covariate (ARIMAX) works well
to forecast volatile demand series, but exponential smoothing with covariate
(ETSX) has a poor performance. Support vector regression (SVR) and dynamic
linear regression (DLR) models generate robust forecasts across different
categories of demands with different CoV values.
