Selection of time series forecasting models based on performance information

By Patrícia M. Santos, Teresa B Ludermir, Ricardo Bastos Cavalcante Prudêncio. 2004

20 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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

1. Radwa El Shawi
2. Sherif Sakr
3. Mohamed Maher
4. Christiane Lemke
5. G. R. Bickerton
6. Bogdan Gabrys
7. Joaquin Vanschoren
8. Crina Grosan

In Management Science and Operations Research

1. Christiane Lemke
2. Bogdan Gabrys
3. Joaquin Vanschoren
4. Sherif Sakr
5. Radwa El Shawi
6. Mohamed Maher

Citations in the corpus

• Automated Machine Learning: State-of-The-Art and Open Challenges (2019, methodology): “Meta models can also be used to rank a particular set of configurations by using the K−nearest neighbour model on the meta features of prior tasks and predicting the top k tasks that are similar to new task tnew and then ranking the best set of configurations of these similar tasks [19, 32].”
• Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2017, methodology): “Several techniques use k-nearest neighbors (Brazdil et al., 2003; dos Santos et al., 2004), and compute the average rank (or success rate ratio’s or significant wins) over all similar prior datasets (Soares and Brazdil, 2000; Brazdil and Soares, 2000).”
• Meta-learning for time series forecasting and forecast combination (2010, methodology): “A newer approach that facilitates combinations on a higher level, the meta learning level, is presented in [7] and applied to time series forecasting in [31].”