Distinguishing between low-dimensional dynamics and randomness in measured time series
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Machine Learning
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Write off-loading: Practical power management for enterprise storage
ACM Transactions on Storage (TOS)
Blind Extraction of Chaotic Sources from White Gaussian Noise Based on a Measure of Determinism
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Prediction of dynamical, nonlinear, and unstable process behavior
The Journal of Supercomputing
On the discrimination of patho-physiological states in epilepsy by means of dynamical measures
Computers in Biology and Medicine
A novel approach for distributed application scheduling based on prediction of communication events
Future Generation Computer Systems
Adaptive chaotic noise reduction method based on dual-lifting wavelet
Expert Systems with Applications: An International Journal
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By modelling the outputs produced by real world systems, we can study and, therefore, understand how they work and behave under different circumstances. This is especially interesting to support the prediction of future behaviour and, consequently, decision-making, what is particularly required in certain application domains. In order to proceed with such modelling, we organise system outputs as time series and study how those series were generated. The study of the time series generation process typically requires specialists and also detailed information on how and where data was obtained from. However, none of them may be available in certain circumstances. Such limitations motivated this paper which presents a survey of techniques commonly used to evaluate and classify time series generation processes and, most importantly, a novel automatic and systematic approach to conduct such task with a minimum of human intervention and subjectivity. By using such approach, researchers can select adequate techniques to model time series, reducing the modelling time and improving the chances to obtain higher accuracy.