Time-series Approaches to Change-prone Class Prediction Problem

2020 ◽  
Author(s):  
Cristiano Melo ◽  
Matheus Lima da Cruz ◽  
Antônio Martins ◽  
José Filho ◽  
Javam Machado
Keyword(s):  
Time Series ◽  
Prediction Problem ◽  
Class Prediction

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Protein structural class prediction using predicted secondary structure and hydropathy profile

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Secondary Structure ◽  
Classification Problem ◽  
Support Vector ◽  
Prediction Problem ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Vector Machines ◽  
Structural Classes ◽  
New Strategies

This thesis explores machine learning models based on various feature sets to solve the protein structural class prediction problem which is a significant classification problem in bioinformatics. Knowledge of protein structural classes contributes to an understanding of protein folding patterns, and this has made structural class prediction research a major topic of interest. In this thesis, features are extracted from predicted secondary structure and hydropathy sequence using new strategies to classify proteins into one of the four major structural classes: all-α, all-β, α/β, and α+β. The prediction accuracy using these features compares favourably with some existing successful methods. We use Support Vector Machines (SVM), since this learning method has well-known efficiency in solving this classification problem. On a standard dataset (25PDB), the proposed system has an overall accuracy of 89% with as few as 22 features, whereas the previous best performing method had an accuracy of 88% using 2510 features.

Modeling Length of Stay as an Optimized Two-class Prediction Problem

2007 ◽  
Vol 46 (03) ◽  
pp. 352-359 ◽  
Author(s):  
N. Peek ◽  
F. Voorbraak ◽  
E. de Jonge ◽  
B. A. J. M. de Mol ◽  
M. Verduijn
Keyword(s):  
Length Of Stay ◽  
Predictive Model ◽  
Model Development ◽  
Outcome Variable ◽  
Prediction Problem ◽  
Threshold Values ◽  
Class Prediction ◽  
Common Strategy ◽  
Risk Patients ◽  
Prediction Problems

Summary Objectives: To develop a predictive model for the outcome length of stay at the Intensive Care Unit (ICU LOS), including the choice of an optimal dichotomization threshold for this outcome. Reduction of prediction problems of this type of outcome to a two-class problem is a common strategy to identify high-risk patients. Methods: Threshold selection and model development are performed simultaneously. From the range of possible threshold values, the value is chosen for which the corresponding predictive model has maximal precision based on the data. To compare the precision of models for different dichotomizations of the outcome, the MALOR performance statistic is introduced. This statistic is insensitive to the prevalence of positive cases in a two-class prediction problem. Results: The procedure is applied to data from cardiac surgery patients to dichotomize the outcome ICU LOS. The class probabilitytree method is used to develop predictive models. Within our data, the best model precision is found at the threshold of seven days. Conclusions: The presented method extends existing procedures for predictive modeling with optimization of the outcome definition for predictive purposes. The method can be applied to all prediction problems where the outcome variable needs to be dichotomized, and is insensitive to changes in the prevalence of positive cases with different dichotomization thresholds.

Genetic Programming Applications in Solving Engineering Problems

2018 ◽  
pp. 243-279
Keyword(s):  
Time Series ◽  
Impact Toughness ◽  
Genetic Programming ◽  
Input Data ◽  
Welded Joint ◽  
Binary Classification ◽  
Forecasting Model ◽  
Prediction Problem ◽  
The Third ◽  
Classification Prediction

This chapter explains how to use genetic programming to solve various kinds of problems in different engineering fields. Here, three applications, each of which relevant to a distinct engineering field, are explained. First, the chapter starts with the GP application in mechanical engineering. The application analyzes the use of GP in modelling impact toughness of welded joint components. The experimental results of impact toughness represent the input data to build GP models of each welded joint component individually. The second part of the chapter shows how two recent versions of GPdotNET can be satisfactorily used for a binary classification-prediction problem in civil engineering. This application puts forward a new classification-forecasting model, namely binary GP for teleconnection studies between oceanic and heavily local hydrologic variables. Finally, the third application demonstrates how GP could be applied to solve a time series forecasting problem in the field of electrical engineering.

scholarly journals USING LSTM NETWORK FOR SOLVING THE MULTIDIMENTIONAL TIME SERIES FORECASTING PROBLEM

2021 ◽  
Vol 2 (68) ◽  
pp. 43-48
Author(s):  
M. Obrubov ◽  
S. Kirillova
Keyword(s):  
Time Series ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Time Series Prediction ◽  
Prediction Problem ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Multidimensional Time Series ◽  
Lstm Network

The article discusses using of the recurrent neural networks technology to the multidimensional time series prediction problem. There is an experimental determination of the neural network architecture and its main hyperparameters carried out to achieve the minimum error. The revealed network structure going to be used further to detect anomalies in multidimensional time series.

scholarly journals Optimal Prediction Under Asymmetric Loss

1997 ◽  
Vol 13 (6) ◽  
pp. 808-817 ◽  
Author(s):  
Peter F. Christoffersen ◽  
Francis X. Diebold
Keyword(s):  
Time Series ◽  
Loss Functions ◽  
Time Varying ◽  
Optimal Prediction ◽  
Prediction Problem ◽  
Optimal Point ◽  
Asymmetric Loss ◽  
Optimal Amount ◽  
Conditional Moments ◽  
Prediction Problems

Prediction problems involving asymmetric loss functions arise routinely in many fields, yet the theory of optimal prediction under asymmetric loss is not well developed. We study the optimal prediction problem under general loss structures and characterize the optimal predictor. We compute the optimal predictor analytically in two leading tractable cases and show how to compute it numerically in less tractable cases. A key theme is that the conditionally optimal forecast is biased under asymmetric loss and that the conditionally optimal amount of bias is time varying in general and depends on higher order conditional moments. Thus, for example, volatility dynamics (e.g., GARCH effects) are relevant for optimal point prediction under asymmetric loss. More generally, even for models with linear conditionalmean structure, the optimal point predictor is in general nonlinear under asymmetric loss, which provides a link with the broader nonlinear time series literature.

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