Original Glossary
From Monashforecasting.com
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A
Adaptive response rate
Additive model
Akaike’s Information Criterion (AIC)
Algorithm
Applicability
ARIMA models
ARMA model
Asymptotically unbiased estimator
Autocorrelated errors
Autocorrelation
Autocorrelation function
Autoregressive (AR) model
B
Backcasting
Backward shift operator
Biased estimator
Bayesian Information Criterion (BIC)
Box-Jenkins methodology
Box-Pierce test
Business cycle
C
Census II
Central limit theorem
Chi-square test
Classical decomposition method
Coefficient of determination
Coefficient of variation
Confidence interval
Correlation coefficient
Correlation matrix
Correlogram
Covariance
Critical value
Crosscorrelation
Cumulative forecasting
Curve fitting
Cyclical data
Cyclical index
D
Decomposition
Degrees of freedom
Delphi method
Dependent variable
Depression
Deseasonalized data
Diagnostic checking
Differencing
Double moving average
Dummy variable
Durbin-Watson statistic
Dynamic regression models
E
Econometric model
Economic indicator
Elasticity
Endogenous variable
Error
Error cost function
Estimation
Ex ante forecast
Ex post forecast
Exogenous variable
Explanatory model
Explanatory variable
Exploratory forecasting
Exponential growth
Exponential smoothing
F
F-test
Feedback
File
Filter
First difference
Forecast horizon
Forecast interval
Forecast variable
Forecasting
Fourier analysis
Function
G
Goodness of fit
Gross National Product
H
Heteroscedasticity
Heuristic
Holdout set
Holt-Winters’ exponential smoothing method
Holt’s exponential smoothing method
Homoscedasticity
Horizontal or stationary data
Hypothesis testing
I
Identification
Impulse response weights
Independent variable
Index numbers
Indicator variable
Integrated
Interactive forecasting
Intercept
Interdependence
Intervention analysis
L
Lag
Lead
Lead time
Leading indicator
Least squares estimation
Likelihood
Ljung-Box test
Local regression
Loess
Logarithmic transformation
Logistic curve
M
M-Competition
M3-IJF Competition
Macrodata
Matrix
Maximum likelihood estimation
Mean
Mean Absolute Percentage Error (MAPE)
Mean Percentage Error (MPE)
Mean Squared Error (MSE)
Medial average
Median
Microdata
Mixed model
Model
Moving average
Multicollinearity
Multiple correlation coefficient
Multiple regression
Multiplicative model
Multivariate ARMA model
N
Naive forecast
Neural networks
Noise
Non-linear estimation
Non-linear forecasting
Non-stationary
Normal distribution
O
Observation
Optimal parameter or weight value
Order selection criteria
Outlier
P
P-value
Parameter
Parsimony
Partial autocorrelation
Partial correlation
Pattern
Pegels’ classification
Polynomial
Polynomial fitting
Post-sample evaluation
Prediction interval
Probability
Product life cycle
Q
Qualitative or technological forecasting
Quantitative forecasting
R
R-bar-squared
R-squared
Random sampling
Random walk
Randomness
Regression
Regression coefficients
Regression with ARIMA errors
Regressor
Residual
S
S-curve
Sample
Sampling distribution
Sampling error
Seasonal adjustment
Seasonal data
Seasonal difference
Seasonal exponential smoothing
Seasonal index
Seasonal variation
Serial correlation
Significance
Simple regression
Slope
Smoothing
Specification error
Spectral analysis
Spencer’s weighted moving average
Standard deviation
Standard error
Standardize
State space modeling
Stationary
Statistic
STL decomposition
T
t-test
Technological forecasting
Time series
Time series model
Tracking signal
Trading day
Transfer function
Transformation
Trend analysis
Turning point
Type of data
U
V
Validation
Variance
Vector ARMA model
W
Weight
White noise
Winters’ exponential smoothing
