## Abstract

Since the beginning of civilizations, the ability to predict future events has been one of the most important abilities and capacities of the human mind, greatly assisting in its survival. The ability to foretell the future has always been a major source of power. On the other hand, the example of Cassandra, the ancient princess who could clearly see and prophesize catastrophic near-future events but was dismissed as insane by her people, underscores the importance of the fact that the forecaster must not only be able to make accurate forecasts, but also convince others of the accuracy of her/his forecasts. In today’s world, the ability to accurately forecast near-term as well as medium-term events such as demand for existing or new products is among the most crucial capacities of an enterprise. In general, the use of forecasts falls under one of three major types: (a) economic forecasts, which attempt to measure and predict macro-economic quantities such as business cycles, inflation rates, money supply and currency exchange rates, (b) technological forecasts, whose main purpose is to predict imminent and upcoming technological break-through and innovation, and to a lesser degree market penetration of completely new products and (c) demand forecasts, whose main purpose is to predict short and medium term sales of existing products, whose sales’ history exists and is accurately recorded.

## Keywords

Artificial Neural Network Forecast Error Forecast Ensemble Exponential Smoothing Seasonal Component## References

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