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:
- How much of the past should be used to predict the next observation
(length of weights)
- The values of the weights.
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)
Thus the correct ARIMA model requires identification of the right number of
lags and the coefficients that should be used.
ARIMA model identification uses autoregressions to identify the underling
Care must be taken to robustly identify and estimate parameters as outliers
(pulses, level shifts , local time trends ) can wreak havoc.
Book: Alan Pankratz
- Forecasting with Univariate Box Jenkins Models : Concepts and Cases -
Book: Jeffrey Wooldridge
- Introductory Econometrics: A Modern Approach -
ARIMA Special Interest Group
Special Interest Group (80 members)
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Compare with: Regression Analysis
| Dynamic Regression
| Exponential Smoothing
| Analytical CRM |
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