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Nobody can really look into the future. But modern statistical methods, econometric models, and Business Intelligence software can to some extent help businesses to forecast and to estimate what is going to happen in the future.
ARIMA stands for AutoRegressive Integrated Moving Average.
The ARIMA Time Series Analysis uses lags and shifts in the historical data
to uncover patterns (e.g. moving averages, seasonality) and predict the future.
The ARIMA model was first developed in the late 60s but it was systemized
by Box and Jenkins in 1976. ARIMA can be more complex to use than other statistical
forecasting techniques, although when implemented properly ARIMA can be quite
powerful and flexible.
ARIMA is a method for determining two things:
For example y(t)= 1/3 * y(t-3) + 1/3 * y(t-2) + 1/3 * y(t-1) is an ARIMA
model; another ARIMA MODEL is y(t)= 1/6 * y(t-3) + 4/6 * y(t-2) + 1/6 * y(t-1)
Book: Alan Pankratz - Forecasting with Univariate Box Jenkins Models : Concepts and Cases
Book: Jeffrey Wooldridge - Introductory Econometrics: A Modern Approach
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