Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals

By Abbas Khosravi, Saeid Nahavandi, Douglas C. Creighton, Amir F. Atiya. IEEE Transactions on Neural Networks, 2011

707 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fad253f283561cd4cd19a732da5288ab1820a21b

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Dipti Srinivasan
2. Abbas Khosravi
3. Amir F. Atiya
4. Saeid Nahavandi
5. Hao Quan
6. Grzegorz Dudek

In Management Science and Operations Research

1. Dipti Srinivasan
2. Hao Quan
3. Abbas Khosravi
4. Mohamed H. Zaki
5. Andy Neely
6. Tim Pearce
7. Alexandra Melike Brintrup
8. Amir F. Atiya

Citations in the corpus

• High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach (2018, methodology): “…it is an ag-glomeration of several prominent works (Tibshirani, 1996; Heskes, 1996; Papadopoulos et al., 2000; Shafer & Vovk, 2008; Mazloumi et al., 2011; Khosravi et al., 2011b; Lak-shminarayanan et al., 2017), each of who presented similar concepts but under different guises and terminology.”
• Occupancy data at different spatial resolutions: Building energy performance and model calibration (2021, background): “Therefore, evaluation of PIs based on coverage alone is subjective and can lead to misleading results [54].”
• Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, methodology): “These advantages have been verified in [20] and [27].”
• Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances (2011, background): “As discussed in [10] and [32], such an assessment is subjective and can lead to misleading results.”