Adaptive Approach and Seasonal Technique for Universal Data Forecasting

Abstract

The main component of observation data includes both
trend and seasonal effects. The represented equation of
forecasting model like ARIMA seems to have more explained
parameters when we need more accuracy in time series
prediction. To apply these elaborate and beautifully crafted
techniques we require an advanced level of knowledge and
sophistication only available from specialists. However, it is
more suitable for anyone who does not familiar with complex
forecasting models can use a simple equation like applied
exponential smoothing model for forecasting. We propose a
simple suitable model that can be applied to most kinds of
data observation types with good prediction outcome. The
proposed model can be applied to a simple spreadsheet
calculation which yields good short term prediction with low
error rate.