Evolving time-series based prediction models for quality criteria in a multi-stage production process

2018 ◽  
Author(s):  
Edwin Lughofer ◽  
Robert Pollak ◽  
Alexandru-Ciprian Zavoianu ◽  
Pauline Meyer-Heye ◽  
Helmut Zorrer ◽  
...  
Keyword(s):  
Time Series ◽  
Production Process ◽  
Prediction Models ◽  
Quality Criteria ◽  
Multi Stage

scholarly journals Multi-Stage Prediction of Feed System Time Series

2021 ◽  
Author(s):  
Lihua Shen ◽  
Biling Wang ◽  
Hongjun Liu
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Tracking Error ◽  
Cnc Machining ◽  
Numerical Control ◽  
Machining Accuracy ◽  
Feed Speed ◽  
Feed System ◽  
Multi Stage ◽  
Robust Prediction

In order to reduce the tracking error of the computer numerical control (CNC) feed system and improve the CNC machining accuracy, a novel prediction model is proposed based on fuzzy C-means robust variational echo state network. Firstly, the feed speed time series is clustered, and then reconstructed for different categories. The multi-stage robust prediction models are established to realize the multi-state robust prediction of the CNC machining feed velocity to reduce the tracking error of the feed system. Finally, the reference and actual time series with different feed speed are used to verify the established models. The results show that the proposed method can reduce the tracking error and realize the effective prediction of the time series of the feed system.

Seismic Response of Embankment Dams Based on Recorded Strong-Motion Data in Japan

2019 ◽  
Vol 35 (2) ◽  
pp. 955-976 ◽  
Author(s):  
DongSoon Park ◽  
Tadahiro Kishida
Keyword(s):  
Time Series ◽  
Seismic Response ◽  
Prediction Models ◽  
Strong Motion ◽  
Dynamic Responses ◽  
Design Stage ◽  
Ground Acceleration ◽  
Motion Data ◽  
Embankment Dams ◽  
Strong Motion Database

It is important to investigate strong-motion time series recorded at dams to understand their complex seismic responses. This paper develops a strong-motion database recorded at existing embankment dams and analyzes correlations between dam dynamic responses and ground-motion parameters. The Japan Commission on Large Dams database used here includes 190 recordings at the crests and foundations of 60 dams during 54 earthquakes from 1978 to 2012. Seismic amplifications and fundamental periods from recorded time series were computed and examined by correlation with shaking intensities and dam geometries. The peak ground acceleration (PGA) at the dam crest increases as the PGA at the foundation bedrock increases, but their ratio gradually decreases. The fundamental period broadly increases with the dam height and PGA at the foundation bedrock. The nonlinear dam response becomes more apparent as the PGA at the foundation bedrock becomes >0.2 g. The prediction models of these correlations are proposed for estimating the seismic response of embankment dams, which can inform the preliminary design stage.

scholarly journals Information technology for time series forecasting by the method of the forecast scheme synthesis

2021 ◽  
Vol 3 (2) ◽  
pp. 81-86
Author(s):  
O. Yu. Mulesa ◽  
◽  
F. E. Geche ◽  
A. Ye. Batyuk ◽  
O. O. Melnyk ◽  
...  
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Linear Combination ◽  
Optimization Problem ◽  
Quality Criterion ◽  
Quality Criteria ◽  
Time Series Forecasting ◽  
Percentage Error ◽  
Forecast Models

The study is devoted to the development of information technology for forecasting based on time series. It has been found that it is important to develop new models and forecasting methods to improve the quality of the forecast. Information technology is based on the evolutionary method of synthesis of the forecast scheme grounded on basic forecast models. The selected method allows you to consider any number of predictive models that may belong to different classes. For a given time series, the weight coefficients with which the models are included in the resulting forecast scheme are calculated by finding the solution to the optimization problem. The method of constructing the objective function for the optimization problem in the form of a linear combination of forecasting results by basic forecasting models is shown. It is proposed to find the solution to the optimization problem using a genetic algorithm. The result of the method is the forecast scheme, which is a linear combination of basic forecast models. To assess the quality of the forecast, it is suggested to use forecasting errors or forecast volatility calculated as the standard deviation. Forecast quality criteria are selected depending on the context of the task. The use of forecast volatility as a quality criterion, with repeated use of technology, will reduce the deviation of forecast values from real data. The structural scheme of information technology is developed. Structurally, information technology consists of two blocks: data processing and interpretation of the obtained values. The result of the application of the developed information technology is the production rules for determining the predicted value of the studied quantity. Experimental verification of the obtained results was performed. The problem of forecasting the number of religious organizations in Ukraine based on statistical data from 1997 to 2000 has been solved. The autoregression method and the linear regression model were chosen as the basic forecast models. Based on the results of using the developed information technology, the weights of the basic models were calculated. It is demonstrated that the obtained forecast scheme allowed to improve the average absolute percentage error and forecast volatility in comparison with the selected models. Keywords: information technology; time series; forecasting; evolutionary technologies; forecast volatility; synthesis of the forecast scheme.

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

scholarly journals A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

2020 ◽  
Vol 12 (11) ◽  
pp. 4730 ◽  
Author(s):  
Ping Wang ◽  
Hongyinping Feng ◽  
Guisheng Zhang ◽  
Daizong Yu
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Hybrid Model ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Input Vector ◽  
Period Function ◽  
Data Set ◽  
Luenberger Observer ◽  
Extraction Algorithm

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

Time-dependent deformations of concrete columns under different construction load histories

2019 ◽  
Vol 22 (8) ◽  
pp. 1845-1854 ◽  
Author(s):  
Dujian Zou ◽  
Chengcheng Du ◽  
Tiejun Liu ◽  
Jun Teng ◽  
Hanbin Cheng
Keyword(s):  
Prediction Models ◽  
Plain Concrete ◽  
Concrete Columns ◽  
In Situ Monitoring ◽  
Time Dependent ◽  
Construction Stage ◽  
Inappropriate Use ◽  
High Rise ◽  
Multi Stage ◽  
Axial Shortening

The adverse effects caused by differential axial shortening in high-rise buildings have received increasing attention with growing building height. However, the axial shortening analysis still lacks accuracy compared to the in-situ monitoring results of practical high-rise buildings during construction stage. It is imperative to identify the error sources, and the applicability of the current shortening prediction models should be test verified. In this study, 14 plain concrete columns were cast, and the multi-stage load method was applied to approximately simulate the loading history of axial concrete members during construction stage. The time-dependent deformations of loaded concrete specimens were measured, and a comparative analysis was conducted between test results and numerical prediction values. It is found that the measured deformations of multi-stage loading cases are all underestimated compared with predicted results, and this underestimation may be mainly caused by the inappropriate use of elastic modulus. It further indicates that the axial shortening analysis of high-rise buildings tends to underestimate the actual shortening value when the traditional calculation method is used. This study provides a reference for explaining the mismatch between the analytical results and the actual shortening values.

scholarly journals Advanced interpretation of subsidence in Murcia (SE Spain) using A-DInSAR data – modelling and validation

2009 ◽  
Vol 9 (3) ◽  
pp. 647-661 ◽  
Author(s):  
G. Herrera ◽  
J. A. Fernández ◽  
R. Tomás ◽  
G. Cooksley ◽  
J. Mulas
Keyword(s):  
Time Series ◽  
Metropolitan Area ◽  
Prediction Models ◽  
Time History ◽  
Element Model ◽  
Absolute Error ◽  
Sar Interferometry ◽  
Se Spain ◽  
Drought Period ◽  
Aquifer System

Abstract. Subsidence is a natural hazard that affects wide areas in the world causing important economic costs annually. This phenomenon has occurred in the metropolitan area of Murcia City (SE Spain) as a result of groundwater overexploitation. In this work aquifer system subsidence is investigated using an advanced differential SAR interferometry remote sensing technique (A-DInSAR) called Stable Point Network (SPN). The SPN derived displacement results, mainly the velocity displacement maps and the time series of the displacement, reveal that in the period 2004–2008 the rate of subsidence in Murcia metropolitan area doubled with respect to the previous period from 1995 to 2005. The acceleration of the deformation phenomenon is explained by the drought period started in 2006. The comparison of the temporal evolution of the displacements measured with the extensometers and the SPN technique shows an average absolute error of 3.9±3.8 mm. Finally, results from a finite element model developed to simulate the recorded time history subsidence from known water table height changes compares well with the SPN displacement time series estimations. This result demonstrates the potential of A-DInSAR techniques to validate subsidence prediction models as an alternative to using instrumental ground based techniques for validation.

Bankruptcy prediction using multivariate grey prediction models

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Hang Jiang ◽  
Jung-Fa Tsai
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
Grey Prediction ◽  
Grey System ◽  
Content Type ◽  
Business Environments ◽  
The Face ◽  
Financial Decision

PurposeIn the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).Design/methodology/approachAs the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).FindingsExperimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.Practical implicationsAmong artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.Originality/valueApplying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

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