Detecting Seasonal Dependencies in Production Lines for Forecast Optimization

Jin, 2015, Significance and challenges of big data research, Big Data Res., 2, 59, 10.1016/j.bdr.2015.01.006 Soller, 2020, Predicting machine errors based on adaptive sensor data drifts in a real world industrial setup, 1 Soller, 2020, Adaptive error prediction for production lines with unknown dependencies, 227 Hoelzl, 2013, Goal oriented recognition of composed activities for reliable and adaptable intelligence systems, J. Ambient Intell. Humaniz. Comput., 5, 357, 10.1007/s12652-013-0198-3 Halbmayer, 2014, A dynamic service module oriented framework for real-world situation representation, 79 Hillmer, 1982, An arima-model-based approach to seasonal adjustment, J. Am. Stat. Assoc., 77, 63, 10.1080/01621459.1982.10477767 Gardner, 2006, Exponential smoothing: the state of the art—part ii, Int. J. Forecast., 22, 637, 10.1016/j.ijforecast.2006.03.005 Chatfield, 1978, The Holt-Winters forecasting procedure, J. R. Stat. Soc., Ser. C, Appl. Stat., 27, 264 Hylleberg, 1992 Gungor, 2009, Industrial wireless sensor networks: challenges, design principles, and technical approaches, IEEE Trans. Ind. Electron., 56, 4258, 10.1109/TIE.2009.2015754 Lindström, 2017, Towards intelligent and sustainable production: combining and integrating online predictive maintenance and continuous quality control, Proc. CIRP, 63, 443, 10.1016/j.procir.2017.03.099 Yang, 2008, Machine condition prognosis based on regression trees and one-step-ahead prediction, Mech. Syst. Signal Process., 22, 1179, 10.1016/j.ymssp.2007.11.012 Zhang, 2005, Neural network forecasting for seasonal and trend time series, Eur. J. Oper. Res., 160, 501, 10.1016/j.ejor.2003.08.037 Le, 2019, Application of long short-term memory (lstm) neural network for flood forecasting, Water, 11, 1387, 10.3390/w11071387 Anghinoni, 2019, Time series trend detection and forecasting using complex network topology analysis, Neural Netw., 117, 295, 10.1016/j.neunet.2019.05.018 Hirsch, 1982, Techniques of trend analysis for monthly water quality data, Water Resour. Res., 18, 107, 10.1029/WR018i001p00107 Helsel, 2006, Regional Kendall test for trend, Environ. Sci. Technol., 40, 4066, 10.1021/es051650b Hussain, 2019, pymannkendall: a python package for non parametric Mann Kendall family of trend tests, J. Open Sour. Softw., 4, 1556, 10.21105/joss.01556 Compare, 2014, Predictive maintenance by risk sensitive particle filtering, IEEE Trans. Reliab., 63, 134, 10.1109/TR.2014.2299651 Murugan, 2018, Analytical algorithm to detect anomalies with seasonality patterns using machine learning techniques, Int. J. Pure Appl. Math., 119, 149 Torres, 2018, A scalable approach based on deep learning for big data time series forecasting, Integr. Comput.-Aided Eng., 25, 335, 10.3233/ICA-180580 Leys, 2013, Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median, J. Exp. Soc. Psychol., 49, 764, 10.1016/j.jesp.2013.03.013 Hochenbaum Corizzo, 2019, Anomaly detection and repair for accurate predictions in geo-distributed big data, Big Data Res., 16, 18, 10.1016/j.bdr.2019.04.001 Hassani, 2018, Big data and causality, Ann. Data Sci., 5, 133, 10.1007/s40745-017-0122-3 Cleff, 2015 Adhikari Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825 Smola, 2004, A tutorial on support vector regression, Stat. Comput., 14, 199, 10.1023/B:STCO.0000035301.49549.88 Safavian, 1991, A survey of decision tree classifier methodology, IEEE Trans. Syst. Man Cybern., 21, 660, 10.1109/21.97458 Mishra, 2019, Descriptive statistics and normality tests for statistical data, Ann. Card. Anaesth., 22, 67, 10.4103/aca.ACA_157_18 Seabold, 2010, Statsmodels: econometric and statistical modeling with python, vol. 57, 61, 10.25080/Majora-92bf1922-011