Considerable literature has accumulated over the years regarding the combination of
forecasts. The primary conclusion of this line of research is that forecast accuracy can be …

This paper is a critical review of exponential smoothing since the original work by Brown and
Holt in the 1950s. Exponential smoothing is based on a pragmatic approach to forecasting …

Regression analysis makes up a large part of supervised machine learning, and consists of
the prediction of a continuous independent target from a set of other predictor variables. The …

Forecasting is required in many situations. Stocking an inventory may require forecasts of
demand months in advance. Telecommunication routing requires traffic forecasts a few …

This editorial suggests moving beyond relying on the dominant logic of multiple regression
analysis (MRA) toward thinking and using algorithms in advancing and testing theory in …

Surveys show that the mean absolute percentage error (MAPE) is the most widely used
measure of prediction accuracy in businesses and organizations. It is, however, biased …

Classifying forecasting methods as being either of a “machine learning” or “statistical” nature
has become commonplace in parts of the forecasting literature and community, as …

Many accuracy measures have been proposed in the past for time series forecasting
comparisons. However, many of these measures suffer from one or more issues such as …

This paper focuses on the use of Delphi technique in building consensus in practice. More
specifically, it reviews some fuzzy issues regarding the expert's panel selection and the …

Machine learning methods trained on raw numerical time series data exhibit fundamental
limitations such as a high sensitivity to the hyper parameters and even to the initialization of …