Pub Date : 2023-09-04DOI: 10.3390/engproc2023039099
Asko Mononen, Ari Alamäki, Janne Kauttonen, Aarne Klemetti, Anu Passi-Rauste, Harri Ketamo
the 9th International Conference on Time Series and Forecasting
第九届时间序列与预测国际会议
{"title":"Forecasted Self: AI-Based Careerbot-Service Helping Students with Job Market Dynamics","authors":"Asko Mononen, Ari Alamäki, Janne Kauttonen, Aarne Klemetti, Anu Passi-Rauste, Harri Ketamo","doi":"10.3390/engproc2023039099","DOIUrl":"https://doi.org/10.3390/engproc2023039099","url":null,"abstract":"the 9th International Conference on Time Series and Forecasting","PeriodicalId":418712,"journal":{"name":"The 9th International Conference on Time Series and Forecasting","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121169965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.3390/engproc2023039100
J. D. Oladeji, Benita Zulch, J. Yacim
{"title":"Predictive Accuracy of Logit Regression for Data-Scarce Developing Markets: A Nigeria and South Africa Study","authors":"J. D. Oladeji, Benita Zulch, J. Yacim","doi":"10.3390/engproc2023039100","DOIUrl":"https://doi.org/10.3390/engproc2023039100","url":null,"abstract":"","PeriodicalId":418712,"journal":{"name":"The 9th International Conference on Time Series and Forecasting","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117249663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-16DOI: 10.3390/engproc2023039098
Mandar V. Tabib, Kristoffer Skare, Endre Bruaset, A. Rasheed
: A computationally efficient predictive digital twin (DT) of a small-scale greenhouse needs an accurate and faster modelling of key variables such as the temperature field and flow field within the greenhouse. This involves : (a) optimally placing sensors in the experimental set-up and (b) developing fast predictive models. In this work, for a greenhouse set-up, the former requirement fulfilled first by identifying the optimal sensor locations for temperature measurements using the QR column pivoting on a tailored basis. Here, the tailored basis is the low-dimensional representation of hi-fidelity computational fluid dynamics (CFD) flow data, and these tailored basis are obtained using proper orthogonal decomposition (POD). To validate the method, the full temperature field inside the greenhouse is then reconstructed for an unseen parameter (inflow condition) using the temperature values from a few synthetic sensor locations in the CFD model. To reconstruct the flow-fields using a faster predictive model than the hi-fidelity CFD model, a long-short term memory (LSTM) method based on a reduced-order model (ROM) is used. The LSTM learns the temporal dynamics of coefficients associated with the POD-generated velocity basis modes. The LSTM-POD ROM model is used to predict the temporal evolution of velocity fields for our DT case, and the predictions are qualitatively similar to those obtained from hi-fidelity numerical models. Thus, the two data-driven tools have shown potential in enabling the forecasting and monitoring of key variables in a digital twin of a greenhouse. In future work, there is scope for improvements in the reconstruction accuracy by involving deep-learning-based corrective source term approaches.
{"title":"Data-Driven Spatio-Temporal Modelling and Optimal Sensor Placement for a Digital Twin Set-Up","authors":"Mandar V. Tabib, Kristoffer Skare, Endre Bruaset, A. Rasheed","doi":"10.3390/engproc2023039098","DOIUrl":"https://doi.org/10.3390/engproc2023039098","url":null,"abstract":": A computationally efficient predictive digital twin (DT) of a small-scale greenhouse needs an accurate and faster modelling of key variables such as the temperature field and flow field within the greenhouse. This involves : (a) optimally placing sensors in the experimental set-up and (b) developing fast predictive models. In this work, for a greenhouse set-up, the former requirement fulfilled first by identifying the optimal sensor locations for temperature measurements using the QR column pivoting on a tailored basis. Here, the tailored basis is the low-dimensional representation of hi-fidelity computational fluid dynamics (CFD) flow data, and these tailored basis are obtained using proper orthogonal decomposition (POD). To validate the method, the full temperature field inside the greenhouse is then reconstructed for an unseen parameter (inflow condition) using the temperature values from a few synthetic sensor locations in the CFD model. To reconstruct the flow-fields using a faster predictive model than the hi-fidelity CFD model, a long-short term memory (LSTM) method based on a reduced-order model (ROM) is used. The LSTM learns the temporal dynamics of coefficients associated with the POD-generated velocity basis modes. The LSTM-POD ROM model is used to predict the temporal evolution of velocity fields for our DT case, and the predictions are qualitatively similar to those obtained from hi-fidelity numerical models. Thus, the two data-driven tools have shown potential in enabling the forecasting and monitoring of key variables in a digital twin of a greenhouse. In future work, there is scope for improvements in the reconstruction accuracy by involving deep-learning-based corrective source term approaches.","PeriodicalId":418712,"journal":{"name":"The 9th International Conference on Time Series and Forecasting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129232065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-28DOI: 10.3390/engproc2023039097
Bibiana Lanzilotta, Gabriela Mordecki, Pablo Tapie, Joaquín Torres
: As a small and open economy, Uruguay is highly exposed to international and regional shocks that affect domestic uncertainty. To account for this uncertainty, we construct two geometric uncertainty indices (based on the survey of industrial expectations about the economy and the export market) and explore their association with the Uruguayan GDP cycle between 1998 and 2022. Based on the estimated linear ARDL models that showed negative but weak relationships between the uncertainty indices and the GDP cycle, we test for the existence of structural breaks in these relationships. Although we find a significant break in 2003 for both indices and another in 2019 for one of them, Wald tests performed on the non-linear models only confirm the structural break in the early 2000s in the model with the index based on export market expectations. In this non-linear model, we find that the negative influence of uncertainty fades after 2003. The evidence of a differential influence before and after this date remains, even when controlling for the variability in non-tradable domestic prices. Two implications arise from these results. First, the evidence of relevant changes that made the Uruguayan economy less vulnerable from 2003 onward. Second, the importance of the expectation about the future of the export market in the macroeconomic cycle of a small and open economy like Uruguay.
{"title":"Uncertainty and Business Cycle: An Empirical Analysis for Uruguay","authors":"Bibiana Lanzilotta, Gabriela Mordecki, Pablo Tapie, Joaquín Torres","doi":"10.3390/engproc2023039097","DOIUrl":"https://doi.org/10.3390/engproc2023039097","url":null,"abstract":": As a small and open economy, Uruguay is highly exposed to international and regional shocks that affect domestic uncertainty. To account for this uncertainty, we construct two geometric uncertainty indices (based on the survey of industrial expectations about the economy and the export market) and explore their association with the Uruguayan GDP cycle between 1998 and 2022. Based on the estimated linear ARDL models that showed negative but weak relationships between the uncertainty indices and the GDP cycle, we test for the existence of structural breaks in these relationships. Although we find a significant break in 2003 for both indices and another in 2019 for one of them, Wald tests performed on the non-linear models only confirm the structural break in the early 2000s in the model with the index based on export market expectations. In this non-linear model, we find that the negative influence of uncertainty fades after 2003. The evidence of a differential influence before and after this date remains, even when controlling for the variability in non-tradable domestic prices. Two implications arise from these results. First, the evidence of relevant changes that made the Uruguayan economy less vulnerable from 2003 onward. Second, the importance of the expectation about the future of the export market in the macroeconomic cycle of a small and open economy like Uruguay.","PeriodicalId":418712,"journal":{"name":"The 9th International Conference on Time Series and Forecasting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129023691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.3390/engproc2023039096
Sachindev Shetty, V. Gori, Gianni Bagni, Giacomo Veneri
{"title":"Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application","authors":"Sachindev Shetty, V. Gori, Gianni Bagni, Giacomo Veneri","doi":"10.3390/engproc2023039096","DOIUrl":"https://doi.org/10.3390/engproc2023039096","url":null,"abstract":"","PeriodicalId":418712,"journal":{"name":"The 9th International Conference on Time Series and Forecasting","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124489497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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