Encontrados 16 documentos, a visualizar página 1 de 2
A Benchmark of Automated Multivariate Time Series Forecasting Tools for Smart C...
Pereira, Pedro José; Costa, Nuno; Mestre, Pedro; Cortez, PauloMost Smart Cities data come from multiple related sensors. Within this context, multivariate Automated Time Series Forecasting (AutoTSF) tools are valuable for providing predictive analytics for citizens and city rulers. In this paper, we benchmark seven multivariate open-source AutoTSF tools (AutoARIMAX, AutoGluon, FlaML, AutoTS, MFEDOT and HyperTS) and one univariate AutoTSF tool (FEDOT), measuring both their...
A data drift approach to update deployed energy prediction machine learning models
Teixeira, Hélder; Matta, Arthur; Pilastri, André; Ferreira, Luís; Pereira, Pedro José; Gonçalves, Carlos; Cortez, PauloWhile there is an increasing interest in Machine Learning (ML) based solutions, scarce research has been devoted to the deployment and monitoring of ML models. In this work, we address this research gap by proposing a new data drift ML update strategy that only considers changes in the input features. Using the realistic Growing Window (GW) and Rolling Window (RW) ML deployment simulation schemes, we propose tw...
A comparison of automated machine learning tools for predicting energy building...
Soares, Daniela; Pereira, Pedro José; Cortez, Paulo; Gonçalves, CarlosIn this paper, we explore and compare three recently proposed Automated Machine Learning (AutoML) tools (AutoGluon, H2 O, Oracle AutoMLx) to create a single regression model that is capable of predicting smart city energy building consumption values. Using a recently collected one year hourly energy consumption dataset, related with 29 buildings from a Portuguese city, we perform several Machine Learning (ML) c...
AI4CITY - An automated machine learning platform for smart cities
Pereira, Pedro José; Gonçalves, Carlos; Nunes, Lara Lopes; Cortez, Paulo; Pilastri, AndréNowadays, the general interest in Machine Learning (ML) based solutions is increasing. However, to develop and deploy a ML solution often requires experience and it involves developing large code scripts. In this paper, we propose AI4CITY, an automated technological platform that aims to reduce the complexity of designing ML solutions, with a particular focus on Smart Cities applications. We compare our solutio...
Predicting multiple domain queue waiting time via machine learning
Loureiro, Carolina; Pereira, Pedro José; Cortez, Paulo; Guimarães, Pedro; Moreira, Carlos; Pinho, AndréThis paper describes an implementation of the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology for a demonstrative case of human queue waiting time prediction. We collaborated with a multiple domain (e.g., bank, pharmacies) ticket management service software development company, aiming to study a Machine Learning (ML) approach to estimate queue waiting time. A large multiple domain databas...
A comparison of automated time series forecasting tools for smart cities
Pereira, Pedro José; Costa, Nuno; Barros, Margarida; Cortez, Paulo; Durães, Dalila; Silva, António José Linhares; Machado, José ManuelMost smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, T...
Multi-objective grammatical evolution of decision trees for mobile marketing us...
Pereira, Pedro José; Cortez, Paulo; Mendes, RuiThe worldwide adoption of mobile devices is raising the value of Mobile Performance Marketing, which is supported by Demand-Side Platforms (DSP) that match mobile users to advertisements. In these markets, monetary compensation only occurs when there is a user conversion. Thus, a key DSP issue is the design of a data-driven model to predict user conversion. To handle this nontrivial task, we propose a novel Mul...
A comparison of machine learning methods for extremely unbalanced industrial qu...
Pereira, Pedro José; Pereira, Adriana; Cortez, Paulo; Pilastri, André LuizThe Industry 4.0 revolution is impacting manufacturing companies, which need to adopt more data intelligence processes in order to compete in the markets they operate. In particular, quality control is a key manufacturing process that has been addressed by Machine Learning (ML), aiming to improve productivity (e.g., reduce costs). However, modern industries produce a tiny portion of defective products, which re...
An intelligent decision support system for production planning in garments indu...
Ribeiro, Rui; Pilastri, André; Carvalho, Hugo; Matta, Arthur; Pereira, Pedro José; Rocha, Pedro; Alves, Marcelo; Cortez, PauloIn this paper, we propose an Intelligent Decision Support System (IDSS) that combines prediction and optimization for production planning. We worked with a company that provides software for the garments Industry and that had access to real-world data related with a client that works with subcontractors. Using an Automated Machine Learning (AutoML) approach, we firstly target four predictive tasks that are cruc...
Using deep autoencoders for in-vehicle audio anomaly detection
Pereira, Pedro José; Coelho, Gabriel José Dias; Ribeiro, Alexandrine; Matos, Luís Miguel Rocha; Nunes, Eduardo Carvalho; Ferreira, AndréCurrent developments on self-driving cars has led to an increasing interest on autonomous shared taxicabs. While most self-driving car technologies focus on the outside environment, there is also a need to provide in-vehicle intelligence (e.g., detect health and safety issues related with the current car occupants). Set within an R&D project focused on in-vehicle cockpit intelligence, the research presented in ...