2018 article

An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting

ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS (ICDM 2018), Vol. 10933, pp. 104–118.

By: R. Yang n, J. He*, M. Xu n, H. Ni n, P. Jones n & N. Samatova*

Contributors: R. Yang n, J. He*, M. Xu n, H. Ni n, P. Jones n & N. Samatova*

author keywords: EMD; Weighted fuzzy time series; Human learning optimization algorithm; Financial markets forecasting
TL;DR: A new Intelligent Hybrid Weighted Fuzzy (IHWF) time series model to improve forecasting accuracy in financial markets, which are complex nonlinear time-sensitive systems, influenced by many factors. (via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: June 17, 2019

2018 article

Mining Aspect-Specific Opinions from Online Reviews Using a Latent Embedding Structured Topic Model

COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2017, PT II, Vol. 10762, pp. 195–210.

By: M. Xu n, R. Yang n, P. Jones n & N. Samatova n

Contributors: M. Xu n, R. Yang n, P. Jones n & N. Samatova n

TL;DR: This paper proposes a Latent embedding structured Opinion mining Topic model, called the LOT, which can simultaneously discover relevant aspect-level specific opinions from small or large numbers of reviews and to assign accurate sentiment to words. (via Semantic Scholar)
UN Sustainable Development Goal Categories
1. No Poverty (OpenAlex)
Sources: Web Of Science, ORCID
Added: January 28, 2019

2017 article

A Lifelong Learning Topic Model Structured Using Latent Embeddings

2017 11TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC), pp. 260–261.

By: M. Xu n, R. Yang n, S. Harenberg n & N. Samatova n

Contributors: M. Xu n, R. Yang n, S. Harenberg n & N. Samatova n

author keywords: Lifelong learning; Topic modeling; Latent embeddings
TL;DR: A latent-embedding-structured lifelong learning topic model, called the LLT model, to discover coherent topics from a corpus and exploit latent word embeddings to structure the model and mine word correlation knowledge to assist in topic modeling. (via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: August 6, 2018

2017 article

An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting

ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017, Vol. 10604, pp. 595–607.

By: R. Yang n, M. Xu n, J. He*, S. Ranshous n & N. Samatova*

Contributors: R. Yang n, M. Xu n, J. He*, S. Ranshous n & N. Samatova*

author keywords: Weighted fuzzy time series; Human learning optimization algorithm; Financial markets forecasting
TL;DR: An intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting, is presented. (via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: November 26, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.