Adaptive response rate - Adaptive response rate describes a forecasting approach that changes how strongly it reacts to new observations according to evidence of changing conditions.
Analog Filter Design - Analog filter design is the process of specifying a continuous-time system that changes the amplitude or phase of different frequency components of a signal.
ARIMA model - An ARIMA model is a time-series model combining autoregressive terms, differencing and moving-average error terms.
ARMA model - An ARMA model combines autoregressive and moving-average components to describe a stationary time series.
Assumed-density filter - An assumed-density filter is a sequential Bayesian approximation method that keeps the evolving probability distribution within a chosen manageable family.
Augmented Lagrangian - An augmented Lagrangian is an optimization function that combines the original objective, multiplier terms for constraints and an additional penalty for...
Autocorrelated errors - Autocorrelated errors are model errors that are correlated with errors at other times or ordered positions.
Autocorrelation - Autocorrelation is the correlation between a variable and a time-shifted version of itself.
Autocorrelation function - The autocorrelation function, or ACF, gives a time series' autocorrelation across different lags.
B-splines - B-splines are basis functions used to represent piecewise-polynomial curves and surfaces.
Backward shift operator - The backward shift operator is a notation that moves a time-series value back by one observation period.
Box-Jenkins methodology - The Box-Jenkins methodology is an iterative approach to building time-series models, especially ARMA and ARIMA models, through identification, parameter...
Box-Pierce test - The Box-Pierce test is a portmanteau test that assesses whether a sequence has autocorrelation across a chosen set of initial lags.
Classical decomposition method - The classical decomposition method separates a time series into estimated trend-cycle, seasonal and remainder components using moving averages and seasonal...
Coefficient of variation - The coefficient of variation, or CV, is the standard deviation divided by the mean.
Collaborative filtering - Collaborative filtering is a recommendation approach that learns from patterns of interaction between users and items.
Cumulative forecasting - Cumulative forecasting predicts the total amount of a variable over several future periods.
Curve fitting - Curve fitting is the process of choosing parameters of a mathematical function so that the function approximates observed data according to a stated criterion.
Delphi method - The Delphi method is a structured process for collecting and refining judgments from a panel through repeated rounds of questions and controlled feedback.
Diagnostic checking - Diagnostic checking is the examination of a fitted model for signs that its assumptions or structure are inadequate for the intended use.
Difference-equation filtering - Difference-equation filtering computes a discrete-time output from weighted current and past inputs and, in recursive filters, past outputs.
Differencing - Differencing is a time-series transformation that replaces observations with changes between observations at a specified lag.
Error cost function - An error cost function assigns a loss or cost to a forecasting error.
Ex ante forecast - An ex ante forecast is a forecast produced using only information available at the time it is made.
Exogenous variable - An exogenous variable is a variable whose value is treated as determined outside the model or system being analyzed.
Exponential growth - Exponential growth is growth in which a quantity increases by a constant proportion over equal time intervals.
Exponential smoothing - Exponential smoothing is a family of methods that updates estimates of a time series as new observations arrive.
Extended Kalman filter - An extended Kalman filter, or EKF, estimates the evolving hidden state of a system whose dynamics or measurements are nonlinear.
Fast Fourier transform - A fast Fourier transform, or FFT, is an algorithm for computing a discrete Fourier transform efficiently.
Filter - A filter is a rule or system that transforms a sequence of observations to retain, suppress, or reshape particular features.
Filter Design - Filter design is the process of choosing a filter structure and parameters to meet specified requirements for transforming a signal.
Filtering - Filtering is the process of estimating a system's current hidden state from observations available up to the present, in probabilistic AI.
First difference - A first difference is the change between a time-series observation and the immediately preceding observation: Δyt = yt − y(t−1).
Fixed-lag smoothing - Fixed-lag smoothing estimates a hidden state from a fixed number of time steps in the past using observations available through the present.
Forecast horizon - A forecast horizon is the distance from the forecast's information cutoff or origin to the future period being predicted.
Forecasting - Forecasting is the process of estimating future outcomes using information available at the time the forecast is made.
Fourier analysis - Fourier analysis studies signals or functions by expressing them in terms of sinusoidal frequency components.
Fourier transform - A Fourier transform converts a signal or function into a representation organized by frequency.
Holdout set - A holdout set is a portion of data deliberately excluded from a specified stage of model development so that predictions can be evaluated on observations...
Holt-Winters forecasting - Holt-Winters forecasting is a family of exponential-smoothing methods that represents a time series through level, trend, and repeating seasonal components.
Homoscedasticity - Homoscedasticity means that an error or outcome distribution has the same variance across the conditions being considered.
Impulse response weights - Impulse response weights describe how a dynamic model's output responds over successive time lags to a one-time unit input or shock.
Kalman filter - A Kalman filter is a recursive method for estimating a hidden state in a linear dynamic system from noisy observations.
Lagrange dual function - The Lagrange dual function is obtained by taking the infimum of a problem's Lagrangian over its original decision variables, while holding the multipliers...
Lagrangian - A Lagrangian is a function that combines an optimization objective with weighted constraint expressions. The weights are called Lagrange multipliers.
Laplace smoothing - Laplace smoothing, also called add-one smoothing, estimates probabilities from categorical counts by adding one to every category count before normalizing.
Linear interpolation smoothing - Linear interpolation smoothing combines two or more probability distributions using nonnegative weights that sum to one.
Ljung-Box test - The Ljung-Box test is a statistical test that jointly assesses whether a set of autocorrelations, from lag one through a chosen maximum lag, is consistent...