Predictive Minimum Description Length Criterion for Time Series Modeling with Neural Networks

1996 ◽  
Vol 8 (3) ◽  
pp. 583-593 ◽  
Author(s):  
Mikko Lehtokangas ◽  
Jukka Saarinen ◽  
Pentti Huuhtanen ◽  
Kimmo Kaski
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Cross Validation ◽  
Minimum Description Length ◽  
Model Complexity ◽  
Time Series Modeling ◽  
Selection Scheme ◽  
The Neural Network ◽  
Multilayer Perceptron Network ◽  
Validation Technique

Nonlinear time series modeling with a multilayer perceptron network is presented. An important aspect of this modeling is the model selection, i.e., the problem of determining the size as well as the complexity of the model. To overcome this problem we apply the predictive minimum description length (PMDL) principle as a minimization criterion. In the neural network scheme it means minimizing the number of input and hidden units. Three time series modeling experiments are used to examine the usefulness of the PMDL model selection scheme. A comparison with the widely used cross-validation technique is also presented. In our experiments the PMDL scheme and the cross-validation scheme yield similar results in terms of model complexity. However, the PMDL method was found to be two times faster to compute. This is significant improvement since model selection in general is very time consuming.

A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

2010 ◽  
Vol 21 (3) ◽  
pp. 595-610 ◽  
Author(s):  
J. P. Florido ◽  
H. Pomares ◽  
I. Rojas ◽  
J. M. Urquiza ◽  
M. A. Lopez-Gordo
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Deterministic Model ◽  
Time Series Forecasting ◽  
Selection Scheme

Applying Non-Random Block Cross-Validation to Improve Reliability of Model Selection and Evaluation in Hydrology: An illustration using an algorithmic model of seasonal snowpack

2020 ◽  
Author(s):  
Charles Luce ◽  
Abigail Lute
Keyword(s):  
Model Selection ◽  
Cross Validation ◽  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Hydrologic Model ◽  
Model Complexity ◽  
Model Parameters ◽  
Model Transferability ◽  
Validation Methods ◽  
Random Block

<p>A central question in model structural uncertainty is how complex a model should be in order to have greatest generality or transferability.  One school of thought is that models become more general by adding process subroutines.  On the other hand, model parameters and structures have been shown to change significantly when calibrated to different basins or time periods, suggesting that model complexity and model transferability may be antithetical.  An important facet to this discussion is noting that validation methods and data applied to model evaluation and selection may tend to bias answers to this question.  Here we apply non-random block cross-validation as a direct assessment of model transferability to a series of algorithmic space-time models of April 1 snow water equivalent (SWE) across 497 SNOTEL stations for 20 years.  In general, we show that low to moderate complexity models transfer most successfully to new conditions in space and time.  In other words, there is an optimum between overly complex and overly simple models.  Because structures in data resulting from temporal dynamics and spatial dependency in atmospheric and hydrological processes exist, naïvely applied cross-validation practices can lead to overfitting, overconfidence in model precision or reliability, and poor ability to infer causal mechanisms.  For example, random k-fold cross-validation methods, which are in common use for evaluating models, essentially assume independence of the data and would promote selection of more complex models.  We further demonstrate that blocks sampled with pseudoreplicated data can produce similar outcomes.  Some sampling strategies favored for hydrologic model validation may tend to promote pseudoreplication, requiring heightened attentiveness for model selection and evaluation.  While the illustrative examples are drawn from snow modeling, the concepts can be readily applied to common hydrologic modeling issues.</p>

scholarly journals Spherical Minimum Description Length

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 575
Author(s):  
Trevor Herntier ◽  
Koffi Ihou ◽  
Anthony Smith ◽  
Anand Rangarajan ◽  
Adrian Peter
Keyword(s):  
Model Selection ◽  
Parameter Space ◽  
Selection Criteria ◽  
Goodness Of Fit ◽  
Minimum Description Length ◽  
Current Model ◽  
Selection Criterion ◽  
Laplace Approximation ◽  
Model Complexity ◽  
Model Selection Criteria

We consider the problem of model selection using the Minimum Description Length (MDL) criterion for distributions with parameters on the hypersphere. Model selection algorithms aim to find a compromise between goodness of fit and model complexity. Variables often considered for complexity penalties involve number of parameters, sample size and shape of the parameter space, with the penalty term often referred to as stochastic complexity. Current model selection criteria either ignore the shape of the parameter space or incorrectly penalize the complexity of the model, largely because typical Laplace approximation techniques yield inaccurate results for curved spaces. We demonstrate how the use of a constrained Laplace approximation on the hypersphere yields a novel complexity measure that more accurately reflects the geometry of these spherical parameters spaces. We refer to this modified model selection criterion as spherical MDL. As proof of concept, spherical MDL is used for bin selection in histogram density estimation, performing favorably against other model selection criteria.

Meta-Analysis and Time Series Modeling Allow a Systematic Review of Primary HIV-1 Drug-Resistant Prevalence in Latin America and Caribbean

2015 ◽  
Vol 13 (2) ◽  
pp. 125-142 ◽  
Author(s):  
Antonio Coelho ◽  
Ronald Moura ◽  
Ronaldo Silva ◽  
Anselmo Kamada ◽  
Rafael Guimaraes ◽  
...  
Keyword(s):  
Systematic Review ◽  
Time Series ◽  
Latin America ◽  
Meta Analysis ◽  
Drug Resistant ◽  
Time Series Modeling ◽  
Latin America And Caribbean ◽  
Hiv 1

On the use of cross-validation for time series predictor evaluation

2012 ◽  
Vol 191 ◽  
pp. 192-213 ◽  
Author(s):  
Christoph Bergmeir ◽  
José M. Benítez
Keyword(s):  
Time Series ◽  
Cross Validation

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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