LSTM-based Flow Prediction
- Resource Type
- Conference
- Authors
- Song, Yang; Yang, Donghua; Tang, Shihan; Liu, Yun; Li, Mengmeng; Guo, Haifeng; Zheng, Bo; Wang, Hongzhi
- Source
- 2023 9th International Conference on Big Data and Information Analytics (BigDIA) Big Data and Information Analytics (BigDIA), 2023 9th International Conference on. :336-343 Dec, 2023
- Subject
- Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Technological innovation
Time series analysis
Supervised learning
Big Data
Prediction algorithms
Time measurement
Tuning
complex time series
missing data
conditional random field
Stacking
deep learning
- Language
- ISSN
- 2771-6902
This paper introduces an innovative approach for predicting continuous time series variables in the context of production or flow. The proposed method employs an LSTM algorithm enhanced through multivariate tuning. This algorithm refines the conventional LSTM by transforming time series data into supervised learning sequences, tailored to the distinctive features of industrial data. The primary novelty lies in incorporating the concepts of periodic measurement and time window into industrial prediction, particularly when dealing with time series characteristics in industrial data. Experimental results using real-world datasets demonstrate a notable improvement in prediction accuracy, achieving a 54.05% enhancement over the traditional LSTM algorithm.