Loess - Loess is a local regression method that estimates a smooth relationship by fitting low-degree polynomials to nearby observations.
Logarithmic transformation - A logarithmic transformation replaces positive values with their logarithms.
Lomb-Scargle Periodograms - Lomb-Scargle periodograms assess periodic structure by fitting sinusoidal models across a range of frequencies, including when observations are unevenly...
Metalevel state space - A metalevel state space is a representation of the possible internal computational states of an agent or program.
Multicollinearity - Multicollinearity is a strong linear relationship among two or more predictor variables in a model.
Noise-to-signal ratio - Noise-to-signal ratio compares the magnitude of unwanted variation with the magnitude of a signal.
Non-linear forecasting - Non-linear forecasting uses a model in which the predicted quantity has a nonlinear relationship with relevant inputs, such as past observations, past...
Nonstationarity - Nonstationarity occurs when the statistical behavior of a process changes with time or another relevant index.
Object-level state space - An object-level state space is the collection of possible states of the task an intelligent system is trying to solve.
Order selection criteria - Order selection criteria are rules for choosing a model's structural complexity, such as how many autoregressive or moving-average terms a time-series model...
Original time series - An original time series is a sequence of observations presented without seasonal adjustment.
Parsimony - Parsimony is the principle of using no more model complexity than is needed for the task and the evidence.
Partial autocorrelation - Partial autocorrelation at lag k measures the linear relationship between a time-series value and its value k periods earlier after accounting for the...
Phase of Spectrum - The phase of a spectrum is the angle of each nonzero complex frequency component in a signal's Fourier representation.
Polynomial fitting - Polynomial fitting estimates a relationship using a sum of powers of an input variable, such as a + bx + cx squared.
Prediction interval - A prediction interval is a range constructed to cover a future observation, or another specified future quantity, at a stated probability or long-run...
Quantitative forecasting - Quantitative forecasting uses numerical information and explicit mathematical or statistical procedures to estimate future outcomes.
R-bar-squared (R̄2 ) - R-bar-squared, usually called adjusted R-squared, is a regression fit statistic that modifies ordinary R-squared to account for the model's residual degrees...
Rao-Blackwellized particle filter - A Rao-Blackwellized particle filter estimates a changing hidden state by sampling some variables while calculating the conditional distribution of others...
Regression with ARIMA errors - Regression with ARIMA errors is a forecasting model that combines a regression relationship with an ARIMA model for the regression's errors.
Seasonal adjustment - Seasonal adjustment estimates and removes recurring seasonal effects from a time series so that nonseasonal movements are easier to examine.
Seasonal Autoregressive Integrated Moving-Average - Seasonal Autoregressive Integrated Moving-Average, commonly abbreviated SARIMA, is a time-series model that combines ordinary ARIMA terms with terms...
Seasonal difference - A seasonal difference subtracts an earlier observation from the same position in a seasonal cycle from the current observation.
Seasonal effect - A seasonal effect is a systematic variation associated with a recurring position in the calendar or seasonal cycle.
Seasonal exponential smoothing - Seasonal exponential smoothing is a family of forecasting methods that updates a series' level and seasonal pattern as observations arrive.
Seasonal index - A seasonal index summarizes the estimated contribution of a particular position in a recurring seasonal cycle.
Seasonal variation - Seasonal variation is recurring movement in a time series associated with a known seasonal or calendar period.
Short-Time Fourier Transform - The Short-Time Fourier Transform, or STFT, represents how a signal's frequency content changes over time.
Signal detection theory - Signal detection theory is a framework for separating the ability to distinguish a meaningful signal from background noise from the decision rule used to...
Smoothing - Smoothing refers to methods that moderate unstable estimates or use surrounding information to estimate an underlying quantity.
Source-filter model - The source-filter model explains speech as an excitation signal shaped by the acoustic response of the vocal tract.
Specification error - Specification error occurs when a model's chosen structure inadequately represents the relationship or process it is intended to describe.
Spectral Analysis - Spectral analysis studies how a signal's variation is distributed across frequencies.
Spectral decomposition - Spectral decomposition expresses a normal square matrix through an orthonormal basis of eigenvectors and their eigenvalues.
Spectrogram - A spectrogram represents how a signal's frequency content changes over time.
State space - A state space is the set of possible states used to represent a system.
State space modeling - State space modeling describes a changing system using an underlying state and equations connecting that state to observations.
Stationarity - Stationarity is a property describing statistical stability over time.
Stationary process - A stationary process is a stochastic process whose statistical behavior is invariant under shifts in time, according to a specified definition of stationarity.
Switching Kalman filter - A switching Kalman filter estimates a continuous hidden state when a discrete mode can change the system's dynamics or observation model.
Time series - A time series is a sequence of observations associated with times or time intervals.
Time series analysis - Time series analysis is the study of observations indexed by time, with methods that account for their ordering, timing and dependence.
Time Series Forecasting - Time series forecasting estimates future values of a quantity from information available at a specified time.
Time series model - A time series model is a mathematical representation of how observations indexed by time relate to past observations, disturbances, latent states or other...
Tracking signal - A tracking signal is a forecast-monitoring statistic highlighting persistent errors that may indicate bias or a changed forecasting environment.
Transfer function - A transfer function describes how an input is transformed into an output by a specified linear system, including delayed effects.
Trend analysis - Trend analysis examines how the underlying level or direction of a time series changes over a specified period.
Trend chart - A trend chart displays a measured quantity in time or observation order so that changes, recurring patterns and unusual values can be inspected.