A model updating strategy for predicting time series with seasonal patterns

Guajardo, Jose A.; Weber, Richard W.; Miranda, J. Jaime

doi:10.1016/j.asoc.2009.07.005

Cited by 48 publications

(32 citation statements)

References 23 publications

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“…Compared to the series considered in (Cortez, 2010;Crone et al, 2006;Guajardo et al, 2010), our series have far fewer examples of whole cycles (only five years) and exhibit changes in the underlying distribution (e.g. non-linear trend) on the same timescale as our seasonal cycle.…”

Section: Advance Order Informationmentioning

confidence: 99%

“…On the other hand, several authors using ML for seasonal time series prediction do not perform a DS step and instead rely only on the structuring of the attributes to allow the ML to capture seasonality (Cortez, 2010;Crone et al, 2006;Guajardo et al, 2010). Typically for cycle length m, a one-step-ahead prediction is provided with attributes corresponding to the previous m or m ϩ 1 points.…”

Section: Advance Order Informationmentioning

confidence: 99%

See 1 more Smart Citation

Advanced predictive-analysis-based decision support for collaborative logistics networks

Ilie‐Zudor

Ekárt

Kemény

et al. 2015

Supply Chain Management: An International Journal

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For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Laszlo MonostoriResearch Laboratory of Engineering and Management Intelligence, Hungarian Academy of Sciences, Budapest, Hungary Abstract Purpose -The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach -The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings -Aside from providing an extended overview of today's big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications -The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value -The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

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Section: Advance Order Informationmentioning

confidence: 99%

Section: Advance Order Informationmentioning

confidence: 99%

Advanced predictive-analysis-based decision support for collaborative logistics networks

Ilie‐Zudor

Ekárt

Kemény

et al. 2015

Supply Chain Management: An International Journal

View full text Add to dashboard Cite

show abstract

“…Guajardo et al [10] proposed an implicit concept drift handling method for time series which is based on moving window and support vector regression (SVR). A moving window slides through the time series data stream in order to define the training and test sets.…”

Section: Related Workmentioning

confidence: 99%

“…The methods proposed for handling concept drifts can be divided into two main groups: (1) implicit or blinding methods and (2) explicit detection methods. Implicit methods [9], [10] are those that update the decision model in regular intervals, independently of the occurrence of concept drifts. The main issues of these approaches are the potential resource consumption to update the learned model even when the incoming data belong to the same concept and the potential overfitting of the learned model to the data.…”

Section: Introductionmentioning

confidence: 99%

FEDD: Feature Extraction for Explicit Concept Drift Detection in time series

Cavalcante

Minku

Oliveira

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-A time series is a sequence of observations collected over fixed sampling intervals. Several real-world dynamic processes can be modeled as a time series, such as stock price movements, exchange rates, temperatures, among others. As a special kind of data stream, a time series may present concept drift, which affects negatively time series analysis and forecasting. Explicit drift detection methods based on monitoring the time series features may provide a better understanding of how concepts evolve over time than methods based on monitoring the forecasting error of a base predictor. In this paper, we propose an online explicit drift detection method that identifies concept drifts in time series by monitoring time series features, called Feature Extraction for Explicit Concept Drift Detection (FEDD). Computational experiments showed that FEDD performed better than error-based approaches in several linear and nonlinear artificial time series with abrupt and gradual concept drifts.

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“…Cheng (2010) derived a limit state function and defined a failure probability to assess the model of a suspension bridge by improving NN and genetic algorithm. Guajardo et al (2010) studied how to update structural models when some new measured data were obtained. Basağa et al (2011) identified the design parameters of a column by NN to make the frequencies of the column in accidence with the measured ones.…”

Section: Introductionmentioning

confidence: 99%

A model updating method for truss structure using stepwise uniform design schemes considered primary factors

Zhang

Chen

Huan-ding

et al. 2014

Lat. Am. j. solids struct.

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Based on the feasibility and reliability of a forward analytical model updating method with uniform design having been proven, this paper studies the effectiveness of the method in the condition that structural measured information is uncertain and incomplete. By taking the experimental data of a steel truss as an example, this paper studies the method of experimental data processing, the determination of structural model with unknown parameters, the interval estimation of identification results and stepwise uniform design considered the correlation among identification parameters. The results show that the errors between the numerical results computed by the updated model and the experimental data are acceptable, which means the method in this paper is feasible and reliable. In the end, based on our experiences and lessons, we summarize a model updating method for complex structures using stepwise uniform design schemes considered the primary and secondary factors, and expound its computational steps.

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A model updating strategy for predicting time series with seasonal patterns

Cited by 48 publications

References 23 publications

Advanced predictive-analysis-based decision support for collaborative logistics networks

Advanced predictive-analysis-based decision support for collaborative logistics networks

FEDD: Feature Extraction for Explicit Concept Drift Detection in time series

A model updating method for truss structure using stepwise uniform design schemes considered primary factors

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