Interpolation in Time Series : An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment
TLDR
A discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data and some suggestions for further research and a new method are proposed.Abstract:
A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate efficiencies of these methods, but uncertainties on the interpolated values are rarely calculated. Furthermore, while they are estimated according to standard methods, the prediction uncertainty is not taken into account: a discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data. Finally, some suggestions for further research and a new method are proposed.read more
Citations
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Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques
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A review of irregular time series data handling with gated recurrent neural networks
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Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry
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TL;DR: Results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures.
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DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection
Santiago Belda,Luca Pipia,Pablo Morcillo-Pallarés,Juan Pablo Rivera-Caicedo,Eatidal Amin,Charlotte De Grave,Jochem Verrelst +6 more
TL;DR: DATimeS expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns and results as an optimum fitting algorithm with most accurate gap-filling performance and associated uncertainties.
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