Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network

被引:7
|
作者
Zhang, Kun [1 ]
Huo, Xing [1 ]
Shao, Kun [2 ]
机构
[1] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China
[2] Hefei Univ Technol, Sch Software, Hefei 230009, Peoples R China
基金
中国国家自然科学基金;
关键词
time-series decomposition; deep learning; neural network; time-series prediction;
D O I
10.3390/math11092060
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Utilizing a temperature time-series prediction model to achieve good results can help us to accurately sense the changes occurring in temperature levels in advance, which is important for human life. However, the random fluctuations occurring in a temperature time series can reduce the accuracy of the prediction model. Decomposing the time-series data prior to performing a prediction can effectively reduce the influence of random fluctuations in the data and consequently improve the prediction accuracy results. In the present study, we propose a temperature time-series prediction model that combines the seasonal-trend decomposition procedure based on the loess (STL) decomposition method, the jumps upon spectrum and trend (JUST) algorithm, and the bidirectional long short-term memory (Bi-LSTM) network. This model can achieve daily average temperature predictions for cities located in China. Firstly, we decompose the time series into trend, seasonal, and residual components using the JUST and STL algorithms. Then, the components determined by the two methods are combined. Secondly, the three components and original data are fed into the two-layer Bi-LSTM model for training purposes. Finally, the prediction results achieved for both the components and original data are merged by learnable weights and output as the final result. The experimental results show that the average root mean square and average absolute errors of our proposed model on the dataset are 0.2187 and 0.1737, respectively, which are less than the values 4.3997 and 3.3349 attained for the Bi-LSTM model, 2.5343 and 1.9265 for the EMD-LSTM model, and 0.9336 and 0.7066 for the STL-LSTM model.
引用
收藏
页数:16
相关论文
共 50 条
  • [11] BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms
    Fan, Yongxin
    Tang, Qian
    Guo, Yangming
    Wei, Yifei
    SENSORS, 2024, 24 (12)
  • [12] Bi-LSTM Model for Time Series Leaf Area Index Estimation Using Multiple Satellite Products
    Liu, Tian
    Jin, Huaan
    Xie, Xinyao
    Fang, Hongliang
    Wei, Dandan
    Li, Ainong
    IEEE Geoscience and Remote Sensing Letters, 2022, 19
  • [13] Bi-LSTM Model for Time Series Leaf Area Index Estimation Using Multiple Satellite Products
    Liu, Tian
    Jin, Huaan
    Xie, Xinyao
    Fang, Hongliang
    Wei, Dandan
    Li, Ainong
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [14] Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network
    Jiaojiao Hu
    Xiaofeng Wang
    Ying Zhang
    Depeng Zhang
    Meng Zhang
    Jianru Xue
    Neural Processing Letters, 2020, 52 : 1485 - 1500
  • [15] NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM
    Peiqiang Gao
    Wenfeng Du
    Qingwen Lei
    Juezhi Li
    Shuaiji Zhang
    Ning Li
    Water Resources Management, 2023, 37 : 1481 - 1497
  • [16] Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network
    Hu, Jiaojiao
    Wang, Xiaofeng
    Zhang, Ying
    Zhang, Depeng
    Zhang, Meng
    Xue, Jianru
    NEURAL PROCESSING LETTERS, 2020, 52 (02) : 1485 - 1500
  • [17] Multivariate Time Series Data Prediction Based on ATT-LSTM Network
    Ju, Jie
    Liu, Fang-Ai
    APPLIED SCIENCES-BASEL, 2021, 11 (20):
  • [18] Vulnerability Time Series Prediction Based on Multivariable LSTM
    Wu, Shuang
    Wang, Congyi
    Zeng, Jianping
    Wu, Chengrong
    2020 IEEE 14TH INTERNATIONAL CONFERENCE ON ANTI-COUNTERFEITING, SECURITY, AND IDENTIFICATION (ASID), 2020, : 185 - +
  • [19] NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN - LSTM
    Gao, Peiqiang
    Du, Wenfeng
    Lei, Qingwen
    Li, Juezhi
    Zhang, Shuaiji
    Li, Ning
    WATER RESOURCES MANAGEMENT, 2023, 37 (04) : 1481 - 1497
  • [20] Prediction for Time Series with CNN and LSTM
    Jin, Xuebo
    Yu, Xinghong
    Wang, Xiaoyi
    Bai, Yuting
    Su, Tingli
    Kong, Jianlei
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC2019), 2020, 582 : 631 - 641