Time series analysis, forecasting and control

By Peter C. Young, S. Shellswell. 1972

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “…was able to win the CIF2016 forecasting competition for monthly time series (Štěpnička 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).”
A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data (2017, methodology): “While others proposed methods based on the time series models such as the ARIMA (Autoregressive Moving Average) model [13]–[16] and the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model [17]–[19].”
Data Poisoning Attacks against Autoregressive Models (2016, methodology): “After centering the series, we learned an order d = 5 AR model via Yule-Walker estimation (Box, Jenkins, and Reinsel 2011).”
Automatic Construction and Natural-Language Description of Nonparametric Regression Models (2014, methodology): “We restricted to regression algorithms for comparability; this excludes models which regress on previous values of times series, such as autoregressive or moving-average models (e.g. Box, Jenkins, and Reinsel, 2013).”
Analysis of photovoltaic system performance time series: Seasonality and performance loss (2015, methodology): “More specifically, seasonal adjustment techniques originating from econometrics, such as classical seasonal decomposition (CSD) [14,15] and regression modeling with Auto-Regressive Integrated Moving Average (ARIMA) [16,17] errors (regARIMA) [18], are used to decompose a seasonal time series into the”
A dendro-ecological study of forest overstorey productivity following the invasion of the non-indigenous shrub Lonicera maackii (2007, methodology): “Time-series models were constructed using maximum likelihood estimates for each tree series (Box & Jenkins 1970), and growth changes were detected by identifying outliers as statistically significant changes in growth pat- terns (i.e., step interventions; Downing & McLaughlin 1990).”
A Review of Deep Learning Models for Time Series Prediction (2019, background): “The results can provide a basis for various applications, e.g., production planning, control, optimization, etc. [1]–[3].”
Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction (2016, methodology): “The artificial neural network (ANN) and Box–Jenkins methods (Box and Jenkins 1970), which include an auto-regressive moving average (ARMA) model, an auto-regressive integrated moving average (ARIMA) model, an auto-regressive (AR) model, and a moving average (MA) model, have been the most widely…”
Prediction intervals in conditionally heteroscedastic time series with stochastic components (2011, methodology): “In this case, following Box and Jenkins (1976), it is common to obtain a stationary transformation by taking differences, and then fit an ARMA model to the differenced series.”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, methodology): “For the time-series model, Huang and Lin [101] and [85] use the ARIMA model [20], da Silva Soares and Prudêncio [49] use ARIMA and other models such as moving averages, and Moradabadi and Meybodi [163] use an approach based on some basic reinforcement learning.”
Improved multivariate portmanteau test (2012, methodology): “In any case, in the overall approach suggested many years ago and presented in their recent book [Box et al., 2008], portmanteau diagnostic checks play a crucial role in constructing time series models.”