Tourist number prediction of historic buildings by singular spectrum analysis

Lyu, Mengning; Yang, Qingshan; Yang, Na; Law, S.S.

doi:10.1080/02664763.2015.1078302

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…For example, Hassani 44 50 found better forecasting performance for U.S. tourist arrivals after using SSA to accurately¯lter the noise from the raw data. Lyu et al 51 analyzed and forecasted the tourist number data of historic buildings by using SSA to decompose the raw data into trend, harmonic and noise components. In particular, some studies also applied SSA to the stock market.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

Xiao

Lee

Huang

et al. 2019

Int. J. Info. Tech. Dec. Mak.

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Stock price exhibits distinct features during different time scales due to the effects of complex factors. Analyzing these features can help delineate the mechanisms that determine the stock price and enhance the prediction accuracy of the stock price. By using singular spectrum analysis (SSA), this paper first decomposes the original price series into a trend component, a market fluctuation component and a noise component to analyze the stock price. The economic meanings of the three components are identified as a long-term trend, effects of significant events and short-term fluctuations caused by noise in the market. Then, to take into account the features of the above three components to the stock price prediction, a novel combined model that integrates SSA and support vector machine (SVM) (e.g., SSA–SVM) is proposed. Compared with SVM, adaptive network-based fuzzy inference system (ANFIS), ensemble empirical mode decomposition-ANFIS (EEMD–ANFIS), EEMD–SVM and SSA–ANFIS, SSA–SVM demonstrates the best prediction performance based on four criteria, indicating that the proposed model is a promising approach for stock price prediction.

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Section: Introductionmentioning

confidence: 99%

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

Xiao

Lee

Huang

et al. 2019

Int. J. Info. Tech. Dec. Mak.

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show abstract

“…To be sure, other methodological frameworks such as spectral decomposition (Coshall, 2000; Lyu et al, 2016), wavelet decomposition (Cao et al, 2016; Wu & Wu, 2019), latent cycle component (Guizzardi & Mazzocchi, 2010), and ensemble empirical mode decomposition (Zhang et al, 2017; Li and Law, 2019) have been used to isolate and study cyclical patterns in individual tourism demand time series. Furthermore, Vatsa (2020) isolates trends and cycles in tourism demand using a multivariate decomposition method developed by Vahid and Engle (1993); Narayan (2011) uses the same method to study the importance of transitory and permanent shocks to the real GDP and tourism demand in AU.…”

Section: Literature Reviewmentioning

confidence: 99%

A new perspective on the linkages between tourism demand and business cycles

Vatsa

Balli

Saleh

2021

Journal of Tourism Research

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“…Spectral techniques (Chan & Lim, 2011;Coshall, 2000aCoshall, , 2000bEeckels, Filis, & Leon, 2012;Lyu, Yang, Na, & Law, 2016), which yield sine and cosine functional representations of the sub-components of time-series, have also been used widely to study trends and patterns in tourism demand: cyclicality and seasonality-two of its characteristic features-have received considerable attention. However, often, a distinction between the two is not drawn-seasonality itself is classified as a cyclical feature.…”

Section: Tourism Demand and Time-series Decompositionmentioning

confidence: 99%

Comovement amongst the demand for New Zealand tourism

Vatsa

2020

Annals of Tourism Research

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show abstract

Tourist number prediction of historic buildings by singular spectrum analysis

Cited by 12 publications

References 22 publications

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

A new perspective on the linkages between tourism demand and business cycles

Comovement amongst the demand for New Zealand tourism

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