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Chaos and Predictability in Ionospheric Time Series
Entropy, 2023 Modelling the Earth’s ionosphere is a big challenge, due to the complexity of the system. Different first principle models have been developed over the last 50 years, based on ionospheric physics and chemistry, mostly controlled by Space Weather conditions.
Massimo Materassi +4 more
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Polynomial Fuzzy Information Granule-Based Time Series Prediction
Mathematics, 2022 Fuzzy information granulation transfers the time series analysis from the numerical platform to the granular platform, which enables us to study the time series at a different granularity. In previous studies, each fuzzy information granule in a granular
Xiyang Yang +3 more
doaj +1 more source
PREDICTION OF LONG-TERM SENTINEL-1 INSAR TIME SERIES ANALYSIS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 This paper presents an initial analysis of predicting time series derived from long-term interferometric Synthetic Aperture Radar (InSAR) data. Time series analysis provides insights into the temporal evolution, variation, and dynamic nature of events ...
S. Abdikan +7 more
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PREDICTION OF INCOMING ORDERS USING THE LONG SHORT-TERM MEMORY METHOD AT PT. XYZ
JISA (Jurnal Informatika dan Sains), 2021 Currently the need for domestic packaging paper continues to increase, driven by the level of consumer awareness about sustainable packaging. PT XYZ is a local company engaged in the Corrugated Cardboard Box (KKG) industry.
Lukman Irawan +2 more
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Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction
Sensors, 2021 A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT).
Ricardo Petri Silva +3 more
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Sequential Quantile Prediction of Time Series [PDF]
IEEE Transactions on Information Theory, 2011 Motivated by a broad range of potential applications, we address the quantile prediction problem of real-valued time series. We present a sequential quantile forecasting model based on the combination of a set of elementary nearest neighbor-type predictors called "experts" and show its consistency under a minimum of conditions.
Biau, Gérard, Patra, B.
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Technology investigation on time series classification and prediction [PDF]
PeerJ Computer Science, 2022 Time series appear in many scientific fields and are an important type of data. The use of time series analysis techniques is an essential means of discovering the knowledge hidden in this type of data.
Yuerong Tong +9 more
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Predictive analytics beyond time series: Predicting series of events extracted from time series data
Wind Energy, 2022 AbstractRealizing carbon neutral energy generation creates the challenge of accurately predicting time‐series generation data for long‐term capacity planning and for short‐term operational decisions. The key challenges for adopting data‐driven decision‐making, specifically predictive analytics, can be attributed to data volume and velocity. Data volume
Sambeet Mishra +3 more
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Neural Network Ensembles for Time Series Prediction [PDF]
, 2007 Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex
Gabrys, Bogdan, Ruta, Dymitr
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Nonparametric sequential prediction of time series [PDF]
Journal of Nonparametric Statistics, 2010 article + 2 ...
Biau, Gérard +3 more
openaire +3 more sources