The 8th International Conference on Time Series and Forecasting

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspec...

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Detalles Bibliográficos
Otros Autores: Rojas, Ignacio, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Basel : MDPI - Multidisciplinary Digital Publishing Institute 2022.
Materias:
Computer science.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009746866706719
Tabla de Contenidos:
  • Preface to "The 8th International Conference on Time Series and Forecastingnacio Rojas
  • Statement of Peer Review
  • Evaluating a Recurrent Neural Network Model for Predicting Readmission to Cardiovascular ICUs Based on Clinical Time Series Data
  • K-Means Clustering Assisted Spectrum Utilization Prediction with Deep Learning Models
  • Alone We Can Do So Little; Together We Cannot Be Detected
  • ODIN TS: A Tool for the Black-Box Evaluation of Time Series Analytics
  • Cloud-Base Height Estimation Based on CNN and All Sky Images
  • A Hybrid Model of VAR-DCC-GARCH and Wavelet Analysis for Forecasting Volatility
  • Synthetic Subject Generation with Coupled Coherent Time Series Data
  • Price Dynamics and Measuring the Contagion between Brent Crude and Heating Oil (US-Diesel) Pre and Post COVID-19 Outbreak
  • Hybrid K-Mean Clustering and Markov Chain for Mobile Network Accessibility and Retainability Prediction
  • A Multivariate Approach for Spatiotemporal Mobile Data Traffic Prediction
  • An Application of Neural Networks to Predict COVID-19 Cases in Italy
  • Relationship between Stationarity and Dynamic Convergence of Time Series
  • Partitioning of Net Ecosystem Exchange Using Dynamic Mode Decomposition and Time Delay Embedding
  • An Ordinal Procedure to Detect Change Points in the Dependence Structure between
  • On the Prospective Use of Deep Learning Systems for Earthquake Forecasting over Schumann
  • Hadeel Afifi, Mohamed Elmahdy, Motaz El Saban and Mervat Abu-Elkheir
  • Probabilistic Forecasting for Oil Producing Wells Using Seq2seq Augmented Model
  • Towards Time-Series Feature Engineering in Automated Machine Learning for Multi-Step-Ahead Forecasting
  • PV Fault Diagnosis Method Based on Time Series Electrical Signal Analysis
  • Early Detection of Flash Floods Using Case-Based Reasoning
  • Inland Areas, Protected Natural Areas and Sustainable Development
  • Expectation-Maximization Algorithm for Autoregressive Models with Cauchy Innovations
  • Deep Representation Learning for Cluster-Level Time Series Forecasting
  • Elpiniki Papageorgiou, Theofilos Mastos and Angelos Papadopoulos
  • Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset
  • Time Series Clustering of High Gamma Dose Rate Incidents
  • A Dynamic Combination of Theta Method and ATA: Validating on a Real Business Case
  • Limitation of Deep-Learning Algorithm for Prediction of Power Consumption
  • Combination of Post-Processing Methods to Improve High-Resolution NWP Solar Irradiance
  • Mohammed Al Saleh, Beatrice Finance, Yehia Taher, Ali Jaber and Roger Luff
  • Comparative Analysis of Residential Load Forecasting with Different Levels of Aggregation
  • An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting
  • Outliers Impact on Parameter Estimation of Gaussian and Non-Gaussian State Space Models: A Simulation Study
  • Time Series Sampling
  • Modelling a Continuous Time Series with FOU(p) Processes
  • PV Energy Prediction in 24 h Horizon Using Modular Models Based on Polynomial Conversion of the L-Transform PDE Derivatives in Node-by-Node-Evolved Binary-Tree Networks
  • Modelling the Number of Daily Stock Transactions Using a Novel Time Series Model
  • Improving the Predictive Power of Historical Consistent Neural Networks
  • Exploration of Different Time Series Models for Soccer Athlete Performance Prediction
  • The Bootstrap for Testing the Equality of Two Multivariate Stochastic Processes with an Application to Financial Markets
  • Using Forecasting Methods on Crime Data: The SKALA Approach of the State Office for Criminal Investigation of North Rhine-Westphalia
  • Reconstructed Phase Spaces and LSTM Neural Network Ensemble Predictions
  • Dynamic Asymmetric Causality Tests with an Application
  • Coarse Grain Spectral Analysis for the Low-Amplitude Signature of Multiperiodic Stellar
  • Pulsators.

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