The 9th International Conference on Time Series and Forecasting最新文献

Forecasted Self: AI-Based Careerbot-Service Helping Students with Job Market Dynamics 预测自我:基于人工智能的职业机器人服务，帮助学生了解就业市场动态

The 9th International Conference on Time Series and Forecasting Pub Date : 2023-09-04 DOI: 10.3390/engproc2023039099

Asko Mononen, Ari Alamäki, Janne Kauttonen, Aarne Klemetti, Anu Passi-Rauste, Harri Ketamo

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Predictive Accuracy of Logit Regression for Data-Scarce Developing Markets: A Nigeria and South Africa Study 数据稀缺发展中市场的Logit回归预测准确性:尼日利亚和南非研究

The 9th International Conference on Time Series and Forecasting Pub Date : 2023-09-04 DOI: 10.3390/engproc2023039100

J. D. Oladeji, Benita Zulch, J. Yacim

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Data-Driven Spatio-Temporal Modelling and Optimal Sensor Placement for a Digital Twin Set-Up 数据驱动的时空建模和数字孪生装置的最佳传感器放置

The 9th International Conference on Time Series and Forecasting Pub Date : 2023-08-16 DOI: 10.3390/engproc2023039098

Mandar V. Tabib, Kristoffer Skare, Endre Bruaset, A. Rasheed

{"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}

引用次数: 0

Uncertainty and Business Cycle: An Empirical Analysis for Uruguay 不确定性与经济周期:乌拉圭的实证分析

The 9th International Conference on Time Series and Forecasting Pub Date : 2023-07-28 DOI: 10.3390/engproc2023039097

Bibiana Lanzilotta, Gabriela Mordecki, Pablo Tapie, Joaquín Torres

{"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}

引用次数: 0

Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application 汽轮机传感器异常检测的传感器虚拟化——工业应用

The 9th International Conference on Time Series and Forecasting Pub Date : 2023-07-27 DOI: 10.3390/engproc2023039096

Sachindev Shetty, V. Gori, Gianni Bagni, Giacomo Veneri

引用次数: 0