Forecasting the CATS benchmark with the Double Vector Quantization method
By G. Simon, John Aldo Lee, M. Cottrell, Michel Verleysen. 2007
12 citations recorded by the source corpus.
The Double Vector Quantization (DVQ) method, a long-term forecasting method based on the self-organizing maps algorithm, has been used to predict the 100 missing values of the CATS competition data set. An analysis of the proposed time series is provided to estimate the dimension of the auto-regressive part of this nonlinear auto-regressive forecasting method. Based on this analysis experimental results using the DVQ method are presented and discussed. As one of the features of the DVQ method is its ability to predict scalars as well as vectors of values, the number of iterative predictions needed to reach the prediction horizon is further observed. The method stability for the long term allows obtaining reliable values for a rather long-term forecasting horizon.
Subfields: Artificial Intelligence, Economics and Econometrics, Statistical and Nonlinear Physics
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
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Cornelius Weber
- Vincenzo Lomonaco
- Christopher Kanan
- Jun Tani
- Tyler L. Hayes
In Statistical and Nonlinear Physics
Citations in the corpus
- An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, methodology): “The advantage of the SOM-based methods is their ability to achieve long-term predictions at much less expensive computational time [22].”