Intelligent system for time series classification using support vector machines applied to supply-chain

García, Fernando Santa Cecilia; Villalba, Luis Javier García; Portela, Javier

doi:10.1016/j.eswa.2012.02.137

Cited by 29 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kini and Sekhar (2013) present the large margin autoregressive (LMAR) method that uses an AR model for each class and the large margin method for estimation of parameters of AR models. A system which builds groups of time series that share the same forecasting model applied to supply chains is presented by Turrado García et al (2012). The similarity between two time series is defined in the following manner: two series will have the same associated ARIMA model if and only if the autocorrelation and partial autocorrelation functions give similar results in their N first positions.…”

Section: Time-series Classificationmentioning

confidence: 99%

The influence of global constraints on similarity measures for time-series databases

Kurbalija

Radovanović

Geler

et al. 2014

Knowledge-Based Systems

View full text Add to dashboard Cite

A time series consists of a series of values or events obtained over repeated measurements in time. Analysis of time series represents an important tool in many application areas, such as stock-market analysis, process and quality control, observation of natural phenomena, medical diagnosis, etc. A vital component in many types of time-series analyses is the choice of an appropriate distance/similarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. Being of quadratic time complexity, however, global constraints are often employed to limit the search space in the matrix during the dynamic programming procedure, in order to speed up computation. Furthermore, it has been reported that such constrained measures can also achieve better accuracy. In this paper, we investigate four representative timeseries distance/similarity measures based on dynamic programming, namely Dynamic Time Warping (DTW), Longest Common Subsequence (LCS), Edit distance with Real Penalty (ERP) and Edit Distance on Real sequence (EDR), and the effects of global constraints on them when applied via the Sakoe-Chiba band. To better understand the influence of global constraints and provide deeper insight into their advantages and limitations we explore the change of the 1-nearest neighbor graph with respect to the change of the constraint size. Also, we examine how these changes reflect on the classes of the nearest neighbors of time series, and evaluate the performance of the 1-nearest neighbor classifier with respect to different distance measures and constraints. Since we determine that constraints introduce qualitative differences in all considered measures, and that different measures are affected by constraints in various ways, we expect our results to aid researchers and practitioners in selecting and tuning appropriate time-series similarity measures for their respective tasks.

show abstract

Section: Time-series Classificationmentioning

confidence: 99%

The influence of global constraints on similarity measures for time-series databases

Kurbalija

Radovanović

Geler

et al. 2014

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…The authors Benjaoran and Dawood (2005) utilized SVM for categorizing the various risk within the supply chain (Baryannis et al, 2019). The author claims the SVM is an effective approach to monitoring the effect in the supply chain (García et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Integrated Model for Predicting Supply Chain Risk Through Machine Learning Algorithms

Kumar

Sharma

2023

Int. j. math. eng. manag. sci.

View full text Add to dashboard Cite

The machine learning model has become a critical consideration in the supply chain. Most of the companies have experienced vari-ous supply chain risks over the past three years. Earlier risk prediction has been performed by supply chain risk management. In this study, an integrated supply chain operations reference (ISCOR) model has been used to evaluate the organization's supply chain risk. Machine learning (ML) has become a hot topic in research and industry in the last few years. With this motivation, we have moved in the direction of a machine learning-based pathway to predict the supply chain risk. The great attraction of this research is that suppliers will understand the associated risk in the activity. This research includes data pre-processing, feature extraction, data transformation, and missing value replacement. The proposed integrated model involves the support vector machine (SVM), k near-est neighbor (k-NN), random forest (RF), decision tree (DT), multiple linear regression (MLR) algorithms, measured performance, and prediction of supply chain risk. Also, these algorithms have performed a comparative analysis under different aspects. Among the other algorithms, the random forest algorithm achieves an accuracy of 99% and has accomplished superior results with a maxi-mum precision of 0.99, recall of 0.99, and F-score of 0.99 with 1% error rate. The model’s prediction indicates that it can be used to find the supply chain risk. Finally, the limitation and the challenges discussed also provide an outlook for future research direction to perform effective management to mitigate the risk.

show abstract

Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Wang

Huang

Mamo

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

Proton exchange membrane fuel cell (PEMFC) stacks are widely used in mobile and portable applications due to their clean and efficient model of operation. We propose an ensemble model based on a stacked long short‐term memory model that combines three machine‐learning models, including long short‐term memory with attention mechanism, support vector regression, and random forest regression, to improve the degradation prediction of a PEMFC stack. The prediction intervals can be derived using the dropout technique. The proposed model is compared with some existing models using two PEMFC stacks. The results show that the proposed model outperforms the other models in terms of mean absolute percentage error and root mean square error. Regarding the remaining useful life prediction, the proposed model with the sliding window approach can provide better results.

show abstract

Intelligent system for time series classification using support vector machines applied to supply-chain

Cited by 29 publications

References 13 publications

The influence of global constraints on similarity measures for time-series databases

The influence of global constraints on similarity measures for time-series databases

Integrated Model for Predicting Supply Chain Risk Through Machine Learning Algorithms

Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Contact Info

Product

Resources

About