Time Series Prediction: Forecasting the Future and Understanding the Past

By Andreas S. Weigend, Neil A. Gershenfeld. 1994

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Citations in the corpus

Accurate On-line Support Vector Regression (2003, methodology): “Three widely used benchmark time series are employed in both experiments: (1) the Santa Fe Institute Competition time series A ( Weigend & Gershenfeld,1994 ), (2) the Mackey-Glass equation with τ = 17 (Mackey & Glass, 1977), and (3) the yearly average sunspot numbers recorded from 1700 to 1995.”
Multi-step prediction for influenza outbreak by an adjusted long short-term memory (2018, background): “Multi-step-ahead time-series prediction, or simply ‘ multi-step prediction ’ , is an analytical task of predicting a sequence of values in future by analysing observed values in the past [13].”
Neural Networks and Financial Trading and the Efficient Markets Hypothesis (2002, background): “Several sources have reported on the simulation of trading agents based on Artificial Neural Networks (ANNs) (White 1988; Kimoto et al 1990; Yoon & Swales 1991; Weigend & Gershenfeld 1994).”
Applying LSTM to Time Series Predictable through Time-Window Approaches (2000, methodology): “This data is set A from the Santa Fe time series prediction competition [6](1).”
Time-series data mining (2012, methodology): “…tasks include query by content [Faloutsos et al. 1994], anomaly detection [Weiss 2004], motif discovery [Lin et al. 2004], prediction [Weigend and Gershenfeld 1994], clustering [Lin and Keogh 2005], classi.cation [Bakshi and Stephanopoulos 1994], and segmentation [Keogh et al. 2003a].”
Rise and fall patterns of information diffusion: model and implications (2012, methodology): “Nonlinear methods for forecasting tend to be hard to interpret, because they rely on nearest-neighbor search [5], or artificial neural networks [39].”