Double quantization of the regressor space for long-term time series prediction: method and proof of stability

被引：11
|
作者：
Simon, G
Lendasse, A
Cottrell, M
Fort, JC
Verleysen, M
机构：
[1] Catholic Univ Louvain, Machine Learning Grp DICE, B-1348 Louvain, Belgium
[2] Aalto Univ, Neural Networks Res Ctr, Lab Comp & Informat Sci, FIN-02015 Helsinki, Finland
[3] Univ Paris 01, SAMOS MATISSE, CNRS, UMR 8595, F-75634 Paris 13, France
[4] Univ Toulouse 3, Lab Stat & Probabilites, CNRS C55830, F-31062 Toulouse, France
来源：
NEURAL NETWORKS | 2004年 / 17卷 / 8-9期
关键词：
time series; long term forecasting; trend prediction; SOM; method stability proof;
D O I：
10.1016/j.neunet.2004.08.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Kohonen self-organization map is Usually considered as a classification or Clustering tool, with only a few applications in time series prediction. In this paper. a particular time series forecasting method based on Kohonen maps is described. This method has been specifically designed for the prediction of long-term trends. The proof of the stability of the method for long-term forecasting is given, as well as illustrations of the utilization of the method both in the scalar and vectorial cases. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1169 / 1181
页数：13
相关论文
共 50 条
  • [41] Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties
    Mallen, Alex T.
    Lange, Henning
    Kutz, J. Nathan
    INTERNATIONAL JOURNAL OF FORECASTING, 2024, 40 (03) : 859 - 868
  • [42] MONITORING LONG-TERM CHANGE TRENDS IN BOREAL PERMAFROST AREA USING TIME SERIES LANDSAT
    Lee, Yeji
    Jung, Yoon Taek
    Kim, Su-Young
    Park, Sang-Eun
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 233 - 236
  • [43] A Pattern Recognition Method for Short-Term Forecast of the Radiation Situation in Long-Term Manned Space Stations
    Tsetlin V.V.
    Nosovskii A.M.
    Sen'ko O.V.
    Kuznetsova A.V.
    Ol'shanskaya E.V.
    Pattern Recognition and Image Analysis, 2010, 20 (03) : 396 - 403
  • [44] Modelling Long-Term Variability in Daily Air Temperature Time Series for Southern Hemisphere Stations
    Olavi Kärner
    Chris R. de Freitas
    Environmental Modeling & Assessment, 2012, 17 : 221 - 229
  • [45] Research on Modeling Method of Medium-and long-term Wind Power Time Series Based on K-means MCMC Algorithm
    Huang Y.
    Qu K.
    Li C.
    Si G.
    Dianwang Jishu/Power System Technology, 2019, 43 (07): : 2469 - 2476
  • [46] Building Trend Fuzzy Granulation-Based LSTM Recurrent Neural Network for Long-Term Time-Series Forecasting
    Tang, Yuqing
    Yu, Fusheng
    Pedrycz, Witold
    Yang, Xiyang
    Wang, Jiayin
    Liu, Shihu
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 30 (06) : 1599 - 1613
  • [47] LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series Forecasting
    Truchan, Hubert
    Kalfar, Christian
    Ahmadi, Zahra
    PROCEEDINGS OF THE 33RD ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2024, 2024, : 2271 - 2281
  • [48] TF4TF: Multi-semantic modeling within the time-frequency domain for long-term time-series forecasting
    Zhang, Xueer
    Wang, Jing
    Bai, Yunfei
    Zhang, Lu
    Lin, Youfang
    NEUROCOMPUTING, 2025, 617
  • [49] A Long-Term Prediction Method of Computer Parameter Degradation Based on Curriculum Learning and Transfer Learning
    Mao, Yuanhong
    Ma, Zhong
    Liu, Xi
    He, Pengchao
    Chai, Bo
    MATHEMATICS, 2023, 11 (14)
  • [50] Spacecraft short-term fault prediction method based on echo state network time series
    Wang, Dawei
    Tan, Zhiyun
    Li, Naihai
    Liu, Yifan
    Han, Xiaojun
    Liang, Jian
    2018 EIGHTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2018), 2018, : 1583 - 1588
12345