Methodology for long-term prediction of time series
By Antti Sorjamaa, Jin Hao, Nima Reyhani, Yongnan Ji, Amaury Lendasse. Neurocomputing, 2007
449 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ec57533b77353111065c01fedfcca04103f8a08c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mingsheng Long
- Jianmin Wang
- Gianluca Bontempi
- Amir F. Atiya
- Souhaib Ben Taieb
- Haixu Wu
- Jiehui Xu
- Antti Sorjamaa
In Computer Vision and Pattern Recognition
- Mingsheng Long
- Jianmin Wang
- Luc Van Gool
- Dacheng Tao
- Kate Saenko
- Jiashi Feng
- Souhaib Ben Taieb
- Gianluca Bontempi
In Electrical and Electronic Engineering
- Haixu Wu
- Mingsheng Long
- Jianmin Wang
- Souhaib Ben Taieb
- Gianluca Bontempi
- Amir F. Atiya
- Liang Sun
- Tian Zhou
Citations in the corpus
- Long-term time series prediction with the NARX network: An empirical evaluation (2008, unknown): “If the user is interested in a longer prediction horizon, a procedure known as multi-step-ahead or long-term prediction, the model’s output should be fed back to the input regressor for a fixed but finite number of time steps [39].”
- Machine Learning Strategies for Time Series Forecasting (2012, methodology): “The Direct strategy [58,49,18] learns independently H models fh”
- Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting (2021, background): “Many time series forecasting methods start from the classic tools [32, 9].”
- A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting (2016, unknown): “MSE decomposition for the LAR DGP with KNN (I/II).”
- A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2011, methodology): “The oldest and most intuitive forecasting strategy is the Recursive (also called Iterated or Multi-Stage ) strategy (Weigend and Gershenfeld, 1994; Sorjamaa et al., 2007; Cheng et al., 2006; Tiao and Tsay, 1994; Kline, 2004; Hamzaebi et al., 2009).”