The 8th International Conference on Time Series and Forecasting / / Ignacio Rojas [and five others].
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...
Saved in:
VerfasserIn: | |
---|---|
Place / Publishing House: | Basel : : MDPI - Multidisciplinary Digital Publishing Institute,, 2022. |
Year of Publication: | 2022 |
Language: | English |
Physical Description: | 1 online resource (434 pages) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
LEADER | 05364nam a2200301 i 4500 | ||
---|---|---|---|
001 | 993567755904498 | ||
005 | 20230327212340.0 | ||
006 | m o d | ||
007 | cr ||||||||||| | ||
008 | 230327s2022 sz o 000 0 eng d | ||
035 | |a (CKB)5860000000259536 | ||
035 | |a (NjHacI)995860000000259536 | ||
035 | |a (EXLCZ)995860000000259536 | ||
040 | |a NjHacI |b eng |e rda |c NjHacl | ||
050 | 4 | |a QA76 |b .R653 2022 | |
082 | 0 | 4 | |a 004 |2 23 |
100 | 1 | |a Rojas, Ignacio, |e author. | |
245 | 1 | 4 | |a The 8th International Conference on Time Series and Forecasting / |c Ignacio Rojas [and five others]. |
264 | 1 | |a Basel : |b MDPI - Multidisciplinary Digital Publishing Institute, |c 2022. | |
300 | |a 1 online resource (434 pages) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
588 | |a Description based on publisher supplied metadata and other sources. | ||
520 | |a 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. | ||
546 | |a In English. | ||
505 | 0 | |a 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. | |
650 | 0 | |a Computer science. | |
776 | |z 3-0365-5452-1 | ||
906 | |a BOOK | ||
ADM | |b 2023-04-15 12:01:39 Europe/Vienna |f system |c marc21 |a 2022-11-14 04:01:55 Europe/Vienna |g false | ||
AVE | |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5341096280004498&Force_direct=true |Z 5341096280004498 |8 5341096280004498 |