✦ LIBER ✦

Financial Forecasting Using Support Vector Machines

✍ Scribed by Lijuan Cao; Francis E.H Tay

Book ID: 106175812
Publisher: Springer-Verlag
Year: 2001
Tongue: English
Weight: 133 KB
Volume: 10
Category: Article
ISSN: 0941-0643
DOI: 10.1007/s005210170010

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

ε-Descending Support Vector Machines for

ε-Descending Support Vector Machines for Financial Time Series Forecasting

✍ Francis E. H. Tay; L. J. Cao 📂 Article 📅 2002 🏛 Springer US 🌐 English ⚖ 166 KB

Financial decision support using neural

Financial decision support using neural networks and support vector machines

✍ Chih-Fong Tsai 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 318 KB

FLOOD STAGE FORECASTING WITH SUPPORT VEC

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES

✍ Shie-Yui Liong; Chandrasekaran Sivapragasam 📂 Article 📅 2002 🏛 American Water Resources Association 🌐 English ⚖ 203 KB

Forecasting financial condition of Chine

Forecasting financial condition of Chinese listed companies based on support vector machine

✍ Yongsheng Ding; Xinping Song; Yueming Zen 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 400 KB

Support vector machines for short-term e

Support vector machines for short-term electrical load forecasting

✍ Mohamed Mohandes 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 165 KB

Short-term electrical load forecasting plays a vital role in the electric power industries. It ensures the availability of supply of electricity, as well as providing the means of avoiding over-and under-utilization of generating capacity and therefore optimizes energy prices. Several methods have b

Incident detection using support vector

Incident detection using support vector machines

✍ Fang Yuan; Ruey Long Cheu 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 256 KB

This paper presents the applications of a recently developed pattern classifier called support vector machine (SVM) in incident detection. Support vector machine is constructed from a unique learning algorithm that extracts training vectors that lie closest to the class boundary, and makes use of th