Long-Term Prediction Intervals of Time Series

被引：17
|
作者：
Zhou, Zhou [1 ]
Xu, Zhiwei [3 ]
Wu, Wei Biao [2 ]
机构：
[1] Univ Toronto, Dept Stat, Toronto, ON M5S 3G3, Canada
[2] Univ Chicago, Dept Stat, Chicago, IL 60637 USA
[3] Univ Michigan, Dept Comp & Informat Sci, Dearborn, MI 48128 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2010年 / 56卷 / 03期
关键词：
Empirical quantiles; heavy tails; long-memory; long-run prediction; quenched central limit theory; SEQUENTIAL PREDICTION; UNIVERSAL SCHEMES;
D O I：
10.1109/TIT.2009.2039158
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider the problem of predicting aggregates or sums of future values of a process based on its past values. In contrast with the conventional prediction problem in which one predicts a future value given past values of the process, in our setting the number of aggregates can go to infinity with respect to the number of available observations. Consistency and Bahadur representations of the prediction estimators are established. A simulation study is carried out to assess the performance of different prediction estimators.
引用
收藏
页码：1436 / 1446
页数：11
相关论文
共 50 条
  • [31] Long-Term Localization With Time Series Map Prediction for Mobile Robots in Dynamic Environments
    Wang, Lisai
    Chen, Weidong
    Wang, Jingchuan
    2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 8587 - 8593
  • [32] Long-term Prediction of Time Series Based on Fuzzy Cognitive Map And Ensemble Learning
    Zhu, Meishu
    Lu, Wei
    Liu, Xiaodong
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 2459 - 2464
  • [33] Long-term Trend Prediction Algorithm Based on Neural Network for Short Time Series
    Xin Zexi
    Zhang Haiyang
    Yue, Ma
    2019 IEEE INTL CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, BIG DATA & CLOUD COMPUTING, SUSTAINABLE COMPUTING & COMMUNICATIONS, SOCIAL COMPUTING & NETWORKING (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2019), 2019, : 1233 - 1238
  • [34] OPELM and OPKNN in long-term prediction of time series using projected input data
    Sovilj, Dusan
    Sorjamaa, Antti
    Yu, Qi
    Miche, Yoan
    Severin, Eric
    NEUROCOMPUTING, 2010, 73 (10-12) : 1976 - 1986
  • [35] Hidden Markov Models Based Approaches to Long-Term Prediction for Granular Time Series
    Guo, Hongyue
    Pedrycz, Witold
    Liu, Xiaodong
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (05) : 2807 - 2817
  • [36] A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis
    Yang, Xiaoping
    Zhang, Zhongxia
    Zhang, Zhongqiu
    Sun, Liren
    Xu, Cui
    Yu, Li
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016, 2016
  • [37] Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
    Inazumi, Shinya
    Shakya, Sudip
    Chio, Chifong
    Kobayashi, Hideki
    Nontananandh, Supakij
    APPLIED SCIENCES-BASEL, 2023, 13 (03):
  • [38] The Long-Term Prediction of Time Series: A Granular Computing-Based Design Approach
    Ma, Cong
    Zhang, Liyong
    Pedrycz, Witold
    Lu, Wei
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (10): : 6326 - 6338
  • [39] Evolutionary echo state network for long-term time series prediction: on the edge of chaos
    Gege Zhang
    Chao Zhang
    WeiDong Zhang
    Applied Intelligence, 2020, 50 : 893 - 904
  • [40] Long-term Prediction of Blood Pressure Time Series Using Multiple Fuzzy Functions
    Abbasi, Robabeh
    Moradi, Mohammad Hassan
    Molaeezadeh, Seyyedeh Fatemeh
    2014 21TH IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME), 2014, : 124 - 127
12345