Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)

TL;DR: The LG-Trader is proposed which will deal with two major machine learning research problems for stock trading decision support: classifier architecture selection and feature selection simultaneously using a genetic algorithm minimizing a new Weighted Localized Generalization Error (wL-GEM).

TL;DR: Results show that the proposed approach is able to cope with the fluctuation of stock market values and it also yields good forecasting accuracy, so it can be considered as a suitable tool to deal with stock market forecasting problems.

TL;DR: This paper presents an evolutionary approach for the problem of discovering pressure patterns under a quality measure related to wind speed and direction, and shows that the proposed evolutionary approach is able to obtain better results than the WT approach.

TL;DR: A hybrid model of SARIMAX-LSTM is presented, in which the SARIM AX model fits and predicts the data, obtains the fitting residual and prediction results, and then uses the LSTM network to predict the prediction error of the SARimAX model, and modifies the prediction results of the SarIMAX model.

TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.

TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.

TL;DR: Time Series Analysis and Forecasting: principles and practice as mentioned in this paper The Oxford Handbook of Quantitative Methods, Vol. 3, No. 2: Statistical AnalysisTime-Series ForecastingPractical Time-Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series analysisTime Series analysisElements of Nonlinear Time Series analyses and forecastingTime series analysis and forecasting by Example.

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.