Works (4)
Updated: July 5th, 2023 15:36
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.
Contributors: R. Yang n , J. He*, 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.
Contributors: 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.
Contributors: 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.
Contributors: R. Yang n , 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
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