@Article{JICS-16-041,
author = {Xiao , Ting},
title = {A Multi-input Time Series Prediction Model Based on CNN-BLSTM},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {16},
number = {1},
pages = {041--051},
abstract = {<p style="text-align: justify;">Real time series data sets are often composed of multiple variables. For the future trend of data,
not only the historical value of the variables but also other implicit influence factors should be considered. In
this paper, a deep neural network prediction model based on multivariable input multi-step output named
CNN-BLSTM is proposed. CNN-BLSTM is mainly composed of convolutional neural network (CNN) and
bi-directional long short memory network (Bi-LSTM). CNN is used to extract spatial features between
variables of multivariate raw data, and Bi-LSTM is used to extract and encode features in time direction. The
proposed CNN-BLSTM is able to predict the temperature based on a real-life meteorological data. The
experimental results show that the prediction accuracy of the proposed CNN-BLSTM model is significantly
better than several state-of-the-art baseline methods.</p>},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22378.html}
}