Live briefing
Everything about Partial autocorrelation function
Partial correlation of a time series with its lagged values
In time series analysis, the partial autocorrelation function (PACF) gives the partial correlation of a stationary time series with its own lagged values, regressed the values of the time series at all shorter lags. It contrasts with the autocorrelation function, which does not control for other lags. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Partial autocorrelation functionA 5-minute orientation
- Skim a recent paper: Unraveling Time Series Dynamics: Evaluating Partial Autocorrelation Function Distribution and Its ImplicationsInfluential work since 2022
- Pick up a classic book: Getting started with data scienceThe most-reprinted book on the subject
- Read the landmark paper: Characterization of the Partial Autocorrelation FunctionCited 109 times
- Go sideways: Breusch–Godfrey testA closely related topic
- Go sideways: Ljung–Box testA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Characterization of the Partial Autocorrelation Function
Interpreting partial autocorrelation functions of seasonal time series models
Characterization of the partial autocorrelation function of nonstationary time series
Asymptotics for the partial autocorrelation function of a stationary process
Time Series Model Analysis Using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) for E-wallet Transactions during a Pandemic
Canonical Partial Autocorrelation Function of a Multivariate Time Series
The local partial autocorrelation function and some applications
Estimating the differencing parameter via the partial autocorrelation function
🆕 Influential since 2022
Unraveling Time Series Dynamics: Evaluating Partial Autocorrelation Function Distribution and Its Implications
On the partial autocorrelation function for locally stationary time series: characterization, estimation and inference
A Novel Transformer Model for Dam Deformation Prediction Based on Partial Autocorrelation Function–Driven Lag Analysis and Variational Mode Decomposition With Wavelet Thresholding
đź“š Books worth reading
- 📕Getting started with data scienceMurtaza Haider · 2015
- 📕Non-sequential Water Quality ProjectJohn B. Thornes · 1975
EconometricsThomas Andren · 2013
EconometricsThomas Andren · 2013
EconometricsThomas Andren · 2013
Digital Signal Processing · 2006
Encyclopedia of Measurement and Statistics 3-Volume SetNeil J. Salkind · 2006
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
- Breusch–Godfrey test In statistics, the Breusch–Godfrey test is used to assess the validity of some of the modelling assumptions inherent in applying regression-like models…
- Ljung–Box test The Ljung–Box test (named for Greta M. Ljung and George E. P. Box) is a type of statistical test of whether any of a group of autocorrelations of a time…
- Autoregressive model In statistics, an autoregressive (AR) model is a modelled representation of a type of random process. It can be used to describe time-varying processes…
- Durbin–Watson statistic In statistics, the Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors)…
- Conformastatic spacetimes Conformastatic spacetimes refer to a special class of static solutions to Einstein's equation in general relativity. The line element for the conformastatic…
- Correlogram In the analysis of data, a correlogram is a chart of correlation statistics. For example, in time series analysis, a plot of the sample autocorrelations…
- Box–Jenkins method In time series analysis, the Box–Jenkins method, named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA)…
- Spectral density estimation In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density (also…