Multiple-output modeling for multi-step-ahead time series forecasting
By Souhaib Ben Taieb, Antti Sorjamaa, Gianluca Bontempi. 2010
240 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing, Time-Series & Forecasting
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
- Bryan Lim
- Amir F. Atiya
- Souhaib Ben Taieb
- Nicolas Loeff
- Gianluca Bontempi
- Tomas Pfister
- Sercan Ö. Arik
- Antti Sorjamaa
In Management Science and Operations Research
- Souhaib Ben Taieb
- Amir F. Atiya
- Gianluca Bontempi
- Bryan Lim
- Tomas Pfister
- Sercan Ö. Arik
- Antti Sorjamaa
- Christoph Bergmeir
In Signal Processing
- Souhaib Ben Taieb
- Amir F. Atiya
- Bryan Lim
- Gianluca Bontempi
- Tomas Pfister
- Sercan Ö. Arik
- Christoph Bergmeir
- Kasun Bandara
In Time-Series & Forecasting
Citations in the corpus
- Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting (2019, methodology): “In line with traditional methods for multi-horizon forecasting [29, 35], recent deep learning methods can be categorized into iterated approaches using autoregressive models [15, 25, 31] or direct methods using sequence-to-sequence models [13, 39].”
- A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2011, methodology): “Finally, (Ben Taieb et al., 2009, 2010) show that the DIRMO strategy gives better forecasting results than Direct and MIMO strategies when the parameter controlling the degree of dependency between forecasts is correctly identified.”
- A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting (2016, unknown): “The SJOINT strategy [45] takes a middle approach between the DIR and the DIRJOINT strategies.”