Time Series Based Predictive Analytics Modelling: Using MS Excel by Branko Pecar & Glyn Davis
Author:Branko Pecar & Glyn Davis [Pecar, Branko]
Language: eng
Format: epub
Publisher: Branko Pecar
Published: 2016-11-29T08:00:00+00:00
In general, a model described as ARIMA (p,1,0), implies that every differenced value in a time
series is the sum of p previously differenced values, each corrected by some factor (coefficients
). On the other hand, a model described as ARIMA (0,1,q) means that any differenced value in a
time series is a result of the current shock value (a random variable, such as white noise), minus
the previous shock value corrected by some factor . We should remember that the theory behind
exponential smoothing has some similarity with this approach. As a matter of fact, exponentially
weighted models from previous chapters are just possible cases out of a number of models
covered by ARIMA modelling.
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