The 8th International Conference on Time Series and Forecasting / / Ignacio Rojas [and five others].

• VerfasserIn: Rojas, Ignacio,
• Place / Publishing House: Basel : : MDPI - Multidisciplinary Digital Publishing Institute,, 2022.
• Year of Publication: 2022
• Language: English
• Physical Description: 1 online resource (434 pages)

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 aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields.

Table of Contents

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.