Time-Series Prediction

By H. Shimodaira. 2002

142 citations recorded by the source corpus.

How researchers see this paper

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

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 114] used S&P500 as their dataset.”
The kernel recursive least-squares algorithm (2004, methodology): “Numerous learning architectures and algorithms have been thrown at this problem with mixed results (see e.g. [41]).”
Hybridization of intelligent techniques and ARIMA models for time series prediction (2008, background): “• The result of the Box–Jenkins procedure depends greatly on the competence and experience of the expert researchers and is affected by a strong path-dependence [66].”
A neuro-fuzzy computing technique for modeling hydrological time series (2004, background): “Although a time series can be interpreted as an output of a system, it is, by definition, an output of an unknown system (Lopez et al., 1996).”