Sisällysluettelo:
“…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…”
Satakunnan ammattikorkeakoulu
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