Virtual Presentations (ITISE-2022)

Virtual Presentations (ITISE-2019)

  • Hamza Couscous, Abderrahman Benchekroun, Khaled Almaksour, Arnaud Davigny and Dhaker Abbe: Photovoltaic Power Forecasting Using Back-Propagation Artificial Neural Network

  • Behrouz Ehsani-Moghaddam: Likelihood Estimation for Hunter Syndrome using ZIP Model and Simulated Data

  • J. Carlos García-Díaz and Oscar Trull: Double Seasonal Holt-Winters to forecast electricity consumption in a hot-dip galvanizing process

  • Javier Estévez, Xiaodong Liu, Juan A. Bellido-Jiménez and Amanda P. García-Marín: Assessing Wavelet Analysis for Precipitation Forecasts Using Artificial Neural Networks in Mediterranean Coast

  • Silvia María Ojeda, Juan Carlos Bellassai Gauto and Marcos A. Landi: On the Evaluation of Similarity for Time Series

  • Shruti Kaushik, Abhinav Choudhury, Nataraj Dasgupta, Sayee Natarajan, Larry Pickett and Varun Dutt Evaluating Auto-encoder and Principal Component Analysis for Feature Engineering in Electronic Health Records

  • Ahmed Elshami, Aliaa Youssef and Mohamed Fakhr: On-The-Fly Dynamic Ensembles for Time Series Forecasting

  • Abdeljalil Settar, Nadia Idrissi and Mohammed Badaoui: Numerical estimation of GARCH models through a constrained Kalman filter

  • Jeronymo Marcondes Pinto and Emerson Fernandes Marçal : Extreme Learning Machine as a Forecast Combination Method

  • Claudio Inacio and Sergio A. David : Dynamic behavior in the fractional scope of agricultural commodities price series vis-a-vis ethanol prices

  • Tulio Vieira, Paulo Almeida, Magali Meireles and Renato Ribeiro : Improving the management of public transport through modeling and forecasting passenger occupancy rate

  • Raul Carrasco : Copper price variation forecasts using genetic algorithms

  • Karim Aoulad Abdelouarit, Boubker Sbihi and Noura Aknin: Big-Learn 2.5: Using Lucidworks and SolrJ to Improve Online Search in Big Data Environment

  • Naveksha Sood, Usha Rani, Srikanth Swaminathan, George Abraham, Dileep A. D. and Varun Dutt: Applications of Statistical and Machine Learning Methods for Predicting Time-Series Performance of Network Devices

  • Gerald Hsu : Using Time-Series and Forecasting to Manage Type 2 Diabetes Conditions (GH-Method: Math-Physical Medicine)

  • Alexandros Sopasakis : Traffic demand and longer term forecasting from real-time observations

  • Moamen Abbas Mousa Al-Sharifi and Firas Ahmmed Mohammed Al-Mohana : Applying Diebold-Mariano test for performance evaluation between individual and hybrid time series models for modeling bivariate time series data and forecasting the unemployment rate in the USA