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Artefatos

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heimdall: Drift Adaptable Models

  • Post author:Eduardo Ogasawara
  • Post published:01/09/2024
  • Post category:Artefatos

By analyzing streaming datasets, it is possible to observe significant changes in the data distribution or models' accuracy during their prediction (concept drift). The goal of 'heimdall' is to measure…

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harbinger: A Unified Time Series Event Detection Framework

  • Post author:Eduardo Ogasawara
  • Post published:16/08/2024
  • Post category:Artefatos

By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In…

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daltoolbox: Leveraging Experiment Lines to Data Analytics

  • Post author:Eduardo Ogasawara
  • Post published:02/05/2024
  • Post category:Artefatos

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the…

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tspredit: Time Series Prediction Integrated Tuning

  • Post author:Eduardo Ogasawara
  • Post published:03/02/2024
  • Post category:Artefatos

Prediction is one of the most important activities while working with time series. There are many alternative ways to model the time series. Finding the right one is challenging to…

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daltoolboxdp: Python-Based Extensions for Data Analytics Workflows

  • Post author:Eduardo Ogasawara
  • Post published:03/02/2024
  • Post category:Artefatos

Provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. Includes tools for data sampling, transformation, feature selection, balancing strategies (e.g., SMOTE), and…

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gstsm: Generalized Spatial-Time Sequence Miner

  • Post author:Eduardo Ogasawara
  • Post published:20/02/2022
  • Post category:Artefatos

Implementations of the algorithms present article Generalized Spatial-Time Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização…

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TSPred: Functions for Benchmarking Time Series Prediction

  • Post author:Eduardo Ogasawara
  • Post published:02/05/2021
  • Post category:Artefatos

Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for…

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STMotif R Package

  • Post author:Eduardo Ogasawara
  • Post published:05/11/2018
  • Post category:Artefatos

The goal of the STSMotif R package is to allow the discovery and ranking of a motif in spatial-time series quickly and efficiently. Available at CRAN:  https://CRAN.R-project.org/package=STMotif    Code repository…

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Supporting the Learning of Evolution Theory Using an Educational Simulator

  • Post author:Eduardo Ogasawara
  • Post published:20/07/2017
  • Post category:Artefatos

Students: Diego Vaz Caetano, Josué Dias Cardoso and Luana Guimarães Piani Ferreira Collaborators: Raphael Abreu, João Quadros, Joel Santos Advisors: Leonardo Lignani, Eduardo Ogasawara Abstract The use of simulators as…

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Orthographic Educational Game for Portuguese Language Countries

  • Post author:Eduardo Ogasawara
  • Post published:03/11/2016
  • Post category:Artefatos

Authors: Paula Chaves, Luan Paschoal, Tauan Velasco, Tiago Bento Sampaio, Julliany Brandão, Carlos Otávio Schocair, João Quadros, Talita Oliveira, Eduardo Ogasawara Abstract The new orthographic agreement introduces some changes in…

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  • heimdall: Drift Adaptable Models
  • harbinger: A Unified Time Series Event Detection Framework
  • daltoolbox: Leveraging Experiment Lines to Data Analytics
  • tspredit: Time Series Prediction Integrated Tuning
  • daltoolboxdp: Python-Based Extensions for Data Analytics Workflows
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