25 years of time series forecasting

By J. De Gooijer, Rob J Hyndman. 2006

1,640 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

Forecasting at Scale (2018, neutral): “Choice of a distance function should be problem-speciﬁc and De Gooijer and Hyndman [2006] review several such error metrics – in practice we prefer mean absolute percentage error for its interpretability.”
Stock price prediction using LSTM, RNN and CNN-sliding window model (2017, neutral): “The different linear models are AR,ARMA,ARIMA and its variations [2] [3] [4].”
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology (2018, methodology): “De Gooijer and Hyndman (2006), in a review covering 25 years of time series forecasting, list 17 commonly used accuracy measures. Shcherbakov et al (2013) presented a survey of more than twenty forecast error measures.”
Reproducibility in forecasting research (2015, background): “It is also referred to in the well cited reviewbyDe Gooijer & Hyndman (2006) of the most important advancements in the recent history of forecasting.”
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, background): “A recent review of time series forecasting models (De Gooijer and Hyndman, 2006) provides extensive coverage of the classic forecasting models (e.g., exponential smoothing) and some nonlinear models, such as regime-switching (Tong, 1990) and neural network models.”
Using artificial neural network models in stock market index prediction (2011, background): “Gooijer and Hyndman (2006) reviewed the papers about time series forecasting from 1982 to 2005.”
Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals (2011, background): “Unfortunately, many authors wrongly interchange these terminologies [13].”
Forecasting and operational research: a review (2008, background): “The other area of substantial activity has been in nonlinear modelling (for a summary see Section 6 of De Gooijer and Hyndman, 2006).”
Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs (2017, background): “A considerable number of stochastic [1] and machine learning based [2] approaches have been proposed for this problem.”
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2011, background): “Since no assumptions are made about the distribution of the error process, the latter approach is preferred (Clements et al., 2004; Gooijer and Hyndman, 2006).”
Stock market index prediction using deep neural network ensemble (2017, background): “During the past few decades, forecasting of price fluctuations for stock markets have been investigated via various artificial intelligence methods[1–3].”
Machine Learning Strategies for Time Series Forecasting (2012, methodology): “In the same period, several useful nonlinear time series models were proposed such as the bilinear model [44], the threshold autoregressive model [56,54,55] and the autoregressive conditional heteroscedastic (ARCH) model [22] (see [25] and [26] for a review).”