TY - GEN
T1 - Time Series Prediction Based on Random Convolution Kernel
AU - Su, Bing
AU - Yang, Jie
AU - Jiang, Nan
AU - Zheng, Jun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the wake of fast transformation and growth of industrial manufacturing digitization, time series data recording important data of industrial machinery performance and functions has become a key fulcrum for processing and understanding industrial Internet of Things data. The prediction of time series data is an urgent problem in the industry. For the prediction of time series data in industry, the prediction accuracy of traditional machine learning methods is not high, and the training time of complex neural network models is too long to meet industrial needs. To solve this problem encountered in practical applications, this paper uses the Rocket method, which uses a simple linear regressor with random convolution kernels to achieve high accuracy with less computational cost. This method is improved in combination with the characteristics of the industrial dataset used, so that it has an acceptable high model prediction accuracy on the shield dataset. In this paper, we also selected four other time series datasets for evaluation, and our results show that Rocket and improvements to it exhibit higher overall accuracy and adaptability in regression than the state-of-the-art machine learning algorithm XGBoost and the recurrent neural network algorithm LSTM.
AB - In the wake of fast transformation and growth of industrial manufacturing digitization, time series data recording important data of industrial machinery performance and functions has become a key fulcrum for processing and understanding industrial Internet of Things data. The prediction of time series data is an urgent problem in the industry. For the prediction of time series data in industry, the prediction accuracy of traditional machine learning methods is not high, and the training time of complex neural network models is too long to meet industrial needs. To solve this problem encountered in practical applications, this paper uses the Rocket method, which uses a simple linear regressor with random convolution kernels to achieve high accuracy with less computational cost. This method is improved in combination with the characteristics of the industrial dataset used, so that it has an acceptable high model prediction accuracy on the shield dataset. In this paper, we also selected four other time series datasets for evaluation, and our results show that Rocket and improvements to it exhibit higher overall accuracy and adaptability in regression than the state-of-the-art machine learning algorithm XGBoost and the recurrent neural network algorithm LSTM.
KW - Time series prediction
KW - machine learning
KW - random convolution kernels
UR - http://www.scopus.com/inward/record.url?scp=85162890887&partnerID=8YFLogxK
U2 - 10.1109/IMBDKM57416.2023.00012
DO - 10.1109/IMBDKM57416.2023.00012
M3 - Conference contribution
AN - SCOPUS:85162890887
T3 - Proceedings - 2023 International Conference on Intelligent Media, Big Data and Knowledge Mining, IMBDKM 2023
SP - 25
EP - 30
BT - Proceedings - 2023 International Conference on Intelligent Media, Big Data and Knowledge Mining, IMBDKM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Intelligent Media, Big Data and Knowledge Mining, IMBDKM 2023
Y2 - 17 March 2023 through 19 March 2023
ER -