A novel method for forecasting time series based on fuzzy logic and visibility graph
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Time series attracts much attention for its remarkable forecasting potential. This paper discusses how fuzzy logic improves accuracy when forecasting time series using visibility graph and presents a novel method to make more accurate predictions. In the proposed method, historical data is firstly converted into a visibility graph. Then, the strategy of link prediction is utilized to preliminarily forecast the future data. Eventually, the future data is revised based on fuzzy logic. To demonstrate the performance, the proposed method is applied to forecast Construction Cost Index, Taiwan Stock Index and student enrollments. The results show that fuzzy logic is able to improve the accuracy by designing appropriate fuzzy rules. In addition, through comparison, it is proved that our method has high flexibility and predictability. It is expected that our work will not only make contributions to the theoretical study of time series forecasting, but also be beneficial to practical areas such as economy and engineering by providing more accurate predictions.
KeywordsForecasting Time series Fuzzy logic Visibility graph Link prediction
Mathematics Subject Classification40B99
The authors greatly appreciate the reviews’ suggestions and the editor’s encouragement. The work is partially supported by National Natural Science Foundation of China (Grant Nos. 61573290, 61503237).
- Brown RG (1957) Exponential smoothing for predicting demand. In: Operations research. In: Inst operations research management sciences, vol 5145–145. LinthicumGoogle Scholar
- Derde LPG, Cooper BS, Goossens H, Malhotra-Kumar S, Willems RJL, Gniadkowski M, Hryniewicz W et al (2014) Interventions to reduce colonisation and transmission of antimicrobial-resistant bacteria in intensive care units: an interrupted time series study and cluster randomised trial. Lancet Infect Dis 14(1):31–39CrossRefGoogle Scholar
- ENR (2011) Engineering News-Record. http://enr.construction.com/
- Hyndman R, Khandakar Y (2018) Automatic time series forecasting: the forecast package for RGoogle Scholar
- Michas G, Sammonds P, Vallianatos, (2014) Dynamic multifractality in earthquake time series: insights from the Corinth Rift. Greece. Pure Appl Geophys 172(7):1909–1921Google Scholar
- Richard E, Gaiffas S, Vayatis N (2012) Link prediction in graphs with autoregressive features. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., New York, pp 2834–3842Google Scholar
- Yang P, Wang G, Zhang F, Zhou X (2015) Causality of global warming seen from observations: a scale analysis of driving force of the surface air temperature time series in the Northern Hemisphere. Clim Dyn 46(9–10):3197–3204Google Scholar
- Zhou T-T, Jin ND, Gao ZK, Luo YB (2012) Limited penetrable visibility graph for establishing complex network from time series. Acta Phys Sin 61(3):030506Google Scholar