Ratio-based lengths of intervals to improve fuzzy time series forecasting

By K. Huarng, Tiffany Hui-Kuang Yu. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2006

308 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2cad8488b911f249117513600a2cedef635e9269

How researchers see this paper

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

In Economics and Econometrics

1. Xin Yao
2. Kazuyuki Murase
3. Md. Mustafizur Rahman
4. M. Islam
5. Anthony Grace
6. Sang Il Lee
7. Seong Joon Yoo

In Management Science and Operations Research

1. Xin Yao
2. Kazuyuki Murase
3. Md. Mustafizur Rahman
4. M. Islam
5. Anthony Grace
6. Christoph Bergmeir
7. Kasun Bandara
8. Slawek Smyl

Citations in the corpus

• Layered Ensemble Architecture for Time Series Forecasting (2016, methodology): “T IME series forecasting (TSF) is the use of a model or technique to predict future values based on previously observed values [1]–[3].”
• Prediction-based portfolio optimization model using neural networks (2009, background): “However, in the last two decades, many machine learning methods for time series prediction appeared, ranging from neural network models to support vector machines and fuzzy sets [13] [14] [15] [16].”