The 8th International Conference on Time Series and Forecasting

• Other Authors: Rojas, Ignacio (Editor), Pomares, Hector (Editor), Valenzuela, Olga (Editor), Rojas, Fernando (Editor), Herrera, Luis (Editor), Kaufman, Peter (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Information technology industries; Computer science; readmission prediction; intensive care unit (ICU); recurrent neural network (RNN); longshort-term memory (LSTM); machine learning (ML); time series analysis; health forecasting; spectrum; utilization; prediction; time-series; clustering; K-Means; LSTM; CNN; outlier detection; outlier detection in time series; time series clustering; time series cluster evaluation; time series; anomaly detection; predictive maintenance; model evaluation; error diagnosis; convolutional neural network; all sky images; cloud-base height; machinelearning; : financial market volatility; VAR-DCC-GARCH; wavelet-based random forest; forecasting; synthetic data; shareable data; privacy; cross-correlation; DCCA method; oil derivatives; energy; accessibility; retainability; Markov chain; K-mean clustering; mobile data traffic; multivariate prediction; temporal; spatial; COVID-19; time series forecasting; NARNN; ARIMA; dynamic convergence; stationarity; unit root; ecosystem respiration; dynamic mode decomposition with control; time delay embedding; ordinal patterns; structural breaks; non-stationary time series; hydrological data; prediction intervals; seq2seq; oil production; automated machine learning; machine learning; time-series forecasting; PV systems; faults; diagnosis; signal processing; time series data
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.