Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances

By Abbas Khosravi, Saeid Nahavandi, Douglas C. Creighton, Amir F. Atiya. 2011

640 citations recorded by the source corpus.

Subfields: Applied Mathematics, 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 Applied Mathematics

1. Vladlen Koltun
2. Davide Scaramuzza
3. Nam-Ho Kim

In Management Science and Operations Research

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

Citations in the corpus

• Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing (2018, methodology): “Our use of mean-variance estimation is motivated by studies that have shown that it is a computationally efficient way to obtain uncertainty estimates [10].”
• 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.”
• Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, methodology): “[78] reviewed aforementioned methods, and considered combined intervals from the methods.”
• Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, methodology): “Forecasting models that are popularly applied to electrical load and the renewable energy forecasting can be classified into three categories: 1) statistical models, such as aurore-gressive (AR), AR integrated moving average (ARIMA), and exponential smoothing (ES) models [1], [3], [4]; 2) artificial…”
• Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, neutral): “It should be noted that the interval-valued data in this study do not come from noise assumptions as in [19], but rather from the expression of variation or the aggregation of huge databases into a reduced number of groups as in [13, 15-18].”