Time Series Analysis: Forecasting and Control
By G. Box, G. Jenkins, G. Reinsel, G. Ljung. 1978
Subfields: Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Fotios Petropoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- J. De Gooijer
- Zongwu Cai
- Wei Biao Wu
Citations in the corpus
- GAN Inversion: A Survey (2021, background): “, each dimension in a vector variable is conditioned on the previous dimensions like ARMA [171] or ARCH [172], this is an autoregressive flow.”
- Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “Stepnicka and Burda, 2016), outperforming state-of-the-art univariate algorithms such as ETS (Hyndman et al., 2008), BaggedETS (Bergmeir et al., 2016), Theta (Hyndman and Billah, 2003), and ARIMA (Box et al., 2015). When building such global models for a time series database, now the problem arises ”
- Effective Bayesian Modeling of Groups of Related Count Time Series (2014, background): “, 2008) and ARIMA models (Box et al., 2008) assume that observations are realvalued and can take on both positive and negative values.”
- Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (2019, methodology): “ns of forecasting, various methods have been proposed to solve the problem. One of the most prominent models is ARIMA [15]. Its statistical properties as well as the wellknown Box-Jenkins methodology [16] in the model selection procedure make it the first attempt for practitioners. However, its linea”
- A Multi-Horizon Quantile Recurrent Forecaster (2017, methodology): “ price and load. 1 Introduction Classical time series forecasting models aim to predict y t+1 given recent history y :t = (y t; ;y 0). Common approaches include Box-Jenkins method, i.e. ARIMA models (Box et al, 2015). In practice, forecasting problems are far more complex. Many related time series a”
- Time-series forecasting using flexible neural tree model (2005, methodology): “Application to Jenkins–Box time series The gas furnace data (series J) of Box and Jenkins (1970) was recorded from a combustion process of a methane–air mixture.”
- Equations of Motion from a Data Series (1987, methodology): “The modeling methodology of Box and Jenkins[ 52 ] should”