TY - GEN
T1 - Enhanced time series forecasting by means of dynamics boosting for industrial process monitoring
AU - Zurita, Daniel
AU - Sala, Enric
AU - Carino, Jesús A.
AU - Delgado, Miguel
AU - Ortega, Juan A.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/21
Y1 - 2015/10/21
N2 - Time series forecasting represents a critical factor, mainly in the industrial sector, in order to assure the proper operation of the manufacturing processes. In this work, a classical ANFIS forecasting scheme is enhanced by the proposal of a dynamics boosting strategy. First, the objective signal is decomposed by means of the Empirical Mode to highlight the main characteristics functions. Next, the dynamics of the functions in regard to the performance of the ANFIS is analyzed. Thus, the functions are separated into different sets. Then, the forecasting is faced with the employment of multiple ANFIS models focused on different dynamics modes. The performance of the proposed system is validated experimentally. According to the obtained results, the proposed approach outperforms the classical methods and represents a reliable and feasible methodology suitable to multiple applications.
AB - Time series forecasting represents a critical factor, mainly in the industrial sector, in order to assure the proper operation of the manufacturing processes. In this work, a classical ANFIS forecasting scheme is enhanced by the proposal of a dynamics boosting strategy. First, the objective signal is decomposed by means of the Empirical Mode to highlight the main characteristics functions. Next, the dynamics of the functions in regard to the performance of the ANFIS is analyzed. Thus, the functions are separated into different sets. Then, the forecasting is faced with the employment of multiple ANFIS models focused on different dynamics modes. The performance of the proposed system is validated experimentally. According to the obtained results, the proposed approach outperforms the classical methods and represents a reliable and feasible methodology suitable to multiple applications.
KW - Artificial intelligence
KW - Condition monitoring
KW - Fuzzy neural networks
KW - Machine learning
KW - Predictive models
KW - Prognosis
KW - Time series analysis
UR - https://www.scopus.com/pages/publications/84959288531
U2 - 10.1109/DEMPED.2015.7303692
DO - 10.1109/DEMPED.2015.7303692
M3 - Conference contribution
AN - SCOPUS:84959288531
T3 - Proceedings - SDEMPED 2015: IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives
SP - 212
EP - 218
BT - Proceedings - SDEMPED 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2015
Y2 - 1 September 2015 through 4 September 2015
ER -
