Model Selection in Online Learning for Times Series Forecasting.

This paper discusses the problem of selecting model parameters in time series forecasting using aggregation. It proposes a new algorithm that relies on the paradigm of prediction with expert advice, where online and offline autoregressive models are regarded as experts. The desired goal of the proposed aggregation-based algorithm is to perform not worse than the best expert in the hindsight. The theoretical analysis shows that the algorithm has a guarantee that holds for any data sequence. Moreover, the empirical evaluation shows that the algorithm outperforms other popular model selection criteria such as Akaike and Bayesian information criteria on cyclic behaving time series

Use of Discriminant Analysis in Time Series Model Selection

Communication in Physical Sciences 2018, 3(1):61-66 Agada Joseph Oche and Ugwuowo, Fidelis Ifeanyi Received 12 November2018/Accepted 16 December 2018 A systematic approach to time series model selection is very important for reduction of the uncertainties associated with highly subjective and inaccurate method currently being used. Information criteria as a measure of goodness of fit have been criticized because of its statistical inefficiency. In this paper, we develop a rule using discriminant analysis for classification of a time series model from a finite list of parsimonious ARMA (p,q) models. A discriminant function is developed for each of the six alternative ARMA(p,q) models using fifty sets of simulated time series data for each model. An algorithm is developed for the evaluation of discriminant scores and model selection. The selection rule is based on the highest discriminant score among the six alternative models. The method was applied to a real life data and thirty sets of simulated data. The real life application resulted in correct model selection while the simulated data gave 93% correct classification

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity