Estimating the horizon of predictability in time-series predictions using inductive modelling tools

被引:1
|
作者
Lopez, Josefina [2 ]
Cellier, Francois E. [1 ]
Cembrano, Gabriela [3 ]
机构
[1] ETH, Dept Comp Sci, CH-8092 Zurich, Switzerland
[2] Tech Univ Catalonia UPC, Software Dept, Terrassa 08222, Spain
[3] Tech Univ Catalonia UPC, Inst Robot & Ind Informat, Barcelona 08028, Spain
关键词
inductive modelling; time-series prediction; fuzzy inductive reasoning; estimation of predictability horizon;
D O I
10.1080/03081079.2010.536540
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper deals with the assessment of how far into the future a time series can be safely predicted using inductive modelling and extrapolation techniques. Three different time series are used to demonstrate the viability of the approaches presented in the paper: one time series representing the water demand of the city of Barcelona, another characterizing the water demand of a section of the city of Rotterdam, and a third describing weather data for the city of Tucson. Fuzzy inductive reasoning ( FIR) is used to predict future values of these time series on the basis of their own past. FIR predictions come with two different built-in measures of confidence that can be used to obtain a quantitative estimate of how far into the future a time series can be predicted.
引用
收藏
页码:263 / 282
页数:20
相关论文
共 50 条
  • [41] Percolative Learning: Time-Series Predictions from Future Tendencies
    Takaishi, Kazuki
    Kobayashi, Masayuki
    Yanagimoto, Miku
    Nagao, Tomoharu
    2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 1647 - 1652
  • [42] MODELING ERRORS IN TIME-SERIES AND K-STEP PREDICTIONS
    DELPINO, GE
    MARSHALL, P
    LECTURE NOTES IN CONTROL AND INFORMATION SCIENCES, 1986, 87 : 88 - 98
  • [43] Modelling and Forecasting of Greenhouse Whitefly Incidence Using Time-Series and ARIMAX Analysis
    Chiu, Lin-Ya
    Rustia, Dan Jeric Arcega
    Lu, Chen-Yi
    Lin, Ta-Te
    IFAC PAPERSONLINE, 2019, 52 (30): : 196 - 201
  • [44] Modelling air pollution time-series by using wavelet functions and genetic algorithms
    Nunnari, G
    SOFT COMPUTING, 2004, 8 (03) : 173 - 178
  • [45] Phase-Field Modelling of Brittle Fracture Using Time-Series Forecasting
    Minh Ngoc Dinh
    Chien Trung Vo
    Cuong Tan Nguyen
    Ngoc Minh La
    COMPUTATIONAL SCIENCE, ICCS 2022, PT II, 2022, : 266 - 274
  • [46] Time-series parametric modelling using Evolution Strategy with deterministic mutation operators
    Dertimanis, V
    Koulocheris, D
    Vrazopoulos, H
    Kanarachos, A
    INTELLIGENT CONTROL SYSTEMS AND SIGNAL PROCESSING 2003, 2003, : 291 - 296
  • [47] On the modelling of seasonal fluctuations in fish landings using structural time-series approach
    Ravichandran, S
    Prajneshu
    INDIAN JOURNAL OF ANIMAL SCIENCES, 2005, 75 (08): : 982 - 984
  • [48] Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique
    Ferrao, Joao L.
    Earland, Dominique
    Novela, Anisio
    Mendes, Roberto
    Tungadza, Alberto
    Searle, Kelly M.
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (11)
  • [49] A generalized prediction model for improving software reliability using time-series modelling
    Raghuvanshi, Kamlesh Kumar
    Agarwal, Arun
    Jain, Khushboo
    Singh, V. B.
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2022, 13 (03) : 1309 - 1320
  • [50] A generalized prediction model for improving software reliability using time-series modelling
    Kamlesh Kumar Raghuvanshi
    Arun Agarwal
    Khushboo Jain
    V. B. Singh
    International Journal of System Assurance Engineering and Management, 2022, 13 : 1309 - 1320