Nonparametric time series forecasting with dynamic updating
Nonparametric time series forecasting with dynamic updating - Sexy kamera chat srbija
The unreasonable effectiveness of structured random orthogonal embeddings. Abstract: We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation.
[ GPs | Clustering | Graphical Models | MCMC | Semi-Supervised | Non-Parametric | Approximations | Bioinformatics | Information Retreival | RL and Control | Time Series | Network Modelling | Active Learning | Neuroscience | Signal Processing | Machine Vision | Machine Hearing | NLP | Deep Learning | Review ] [ Balog | Bauer | Bui | Dziugaite | Ge | Ghahramani | Gu | Hernández-Lobato | Kilbertus | Kok | Li | Lomeli | Matthews | Navarro | Peharz | Rasmussen | Rojas-Carulla | Rowland | Ścibior | Shah | Steinrücken | Rich Turner | Weller ] [ Borgwardt | Bratières | Calliess | Chen | Cunningham | Davies | Deisenroth | Duvenaud | Eaton | Frellsen | Frigola | Van Gael | Gal | Heaukulani | Heller | Hoffman | Houlsby | Huszár | Knowles | Lacoste-Julien | Lloyd | Lopez-Paz | Mc Allister | Mc Hutchon | Mohamed | Orbanz | Ortega | Palla | Quadrianto | Roy | Saatçi | Tobar | Ryan Turner | Snelson | van der Wilk | Williamson | Wilson ] Gaussian processes are non-parametric distributions useful for doing Bayesian inference and learning on unknown functions.
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(2017) High-dimensional functional time series forecasting, in Functional Statistics and Related Fields, in Aneiros, G., G.
However, the field lacks a principled method to handle streaming data in which the posterior distribution over function values and the hyperparameters are updated in an online fashion.
The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive.
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Hand: Information generation (2007): how data rule our world, Oneworld Publications, Oxford, England, xx 246 pages, ISBN: 978-1-85168-445-8, Computational Statistics, 24(2), 373-374Department of Econometrics and Business Statistics, Monash University.
We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univariate time series forecasting and functional principal component regression techniques.
When data in the most recent year are partially observed, we improve point forecast accuracy using dynamic updating methods.
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We introduce matrices with complex entries which give significant further accuracy improvement.