Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

By G. Box, D. A. Pierce. 1970

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Financial Time Series Prediction Using Deep Learning (2017, methodology): “The Kalman Filter [1] and Auto-Regressive (AR) Models are seminal statistical approaches, where the ARMA [2] and ARIMA [3] (Auto-Regressive Integrated Moving Average) models are further generalizations of the AR, derived by combining the AR models with the moving-average (MA) models.”
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, methodology): “Here, we introduce some representative methods and describe the challenges that remain to be solved.”
Testing the Martingale Difference Hypothesis (2003, methodology): “The test statistic most commonly employed is the [Box and Pierce (1970)] Q statistic for testing that a process is uncorrelated up to a given order p (that is, the first p autocorrelations are equal to zero).”
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks (2017, methodology): “Efforts in this direction range from the early work using naive RNN models [7] and the hybrid models [16, 33, 34] combining the use of ARIMA [3] and”
TESTING FOR WHITE NOISE UNDER UNKNOWN DEPENDENCE AND ITS APPLICATIONS TO DIAGNOSTIC CHECKING FOR TIME SERIES MODELS (2010, methodology): “In the time domain, the most popular test is probably the Box and Pierce (1970) (BP) portmanteau test, which admits the following form:”
Hybridization of intelligent techniques and ARIMA models for time series prediction (2008, methodology): “To determine whether or not a model fits the data well, the Portmanteau test of fit developed by Box and Pierce [7] with the variance correction for small samples introduced by Ljung and Box [34] is used, and in a final file, all the different models selected by the user expert are evaluated.”
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting (2021, unknown): “2 Baselines We compare STODE with following baseline models: • ARIMA [2]: Auto-Regressive Integrated Moving Average model, which is a well-known statistical model of time series analysis.”
Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity (2008, methodology): “classical results, see Box and Pierce (1970) and Li and Mak (1994).”