Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:alternativeTo |
gptkb:exponential_smoothing
state space models |
| gptkbp:appliesTo |
economics
engineering finance weather forecasting sales forecasting |
| gptkbp:assumes |
linearity
normality of errors no autocorrelation in residuals |
| gptkbp:canBe |
maximum likelihood estimation
least squares |
| gptkbp:component |
autoregressive (AR) part
integrated (I) part moving average (MA) part |
| gptkbp:extendsTo |
gptkb:SARIMA_model
seasonal ARIMA (SARIMA) ARIMAX (with exogenous variables) ARIMAX model vector ARIMA (VARIMA) |
| gptkbp:field |
gptkb:machine_learning
statistics econometrics |
| gptkbp:fullName |
AutoRegressive Integrated Moving Average model
|
| gptkbp:hasModel |
non-stationary time series
|
| https://www.w3.org/2000/01/rdf-schema#label |
ARIMA model
|
| gptkbp:introduced |
gptkb:George_Box
gptkb:Gwilym_Jenkins |
| gptkbp:introducedIn |
1970
|
| gptkbp:output |
confidence intervals
forecasted values |
| gptkbp:parameter |
d (degree of differencing)
p (order of AR part) q (order of MA part) |
| gptkbp:publishedIn |
gptkb:Time_Series_Analysis:_Forecasting_and_Control
|
| gptkbp:relatedTo |
gptkb:Box-Jenkins_methodology
stationarity differencing autocorrelation function (ACF) partial autocorrelation function (PACF) |
| gptkbp:requires |
stationarity (after differencing)
|
| gptkbp:software |
gptkb:SAS
gptkb:Python_(statsmodels) gptkb:SPSS R |
| gptkbp:usedFor |
time series forecasting
|
| gptkbp:bfsParent |
gptkb:Forecasting_in_Business_and_Economics
|
| gptkbp:bfsLayer |
6
|