Anomaly detection in specific datasets involves the detection of circumstances that are
common in a homogeneous data. When looking at network traffic data, it is generally difficult …

Artificial Neural networks (ANN) are powerful and effective tools in time-series applications.
The first aim of this paper is to diagnose better and more efficient ANN models (Back …

This work comes as part of the recent continuous and increasing interest in Wavelet
Transforms (WT) and Artificial Neural Networks (ANN). This paper introduces a novel hybrid …

The autoregressive fractional integrated moving average (ARFIMA) has become one of the
popular linear models in time series modeling and forecasting in the past decades. Recent …

Financial markets in any country in the world are one of the most important pillars of the
economy. The global financial crisis and the current economic and political situation have …

The research objective is to discuss the adoption of the wavelet transformation method (WT)
in processing time series, for its efficiency. As well as comparing modern methods …

Artificial neural networks (ANNs) are flexible computing frameworks and universal
approximates that can be applied to a wide range of time series forecasting problems with a …

The goal of this paper is to predict electrical energy consumption using nonlinear
autoregressive (NAR) models. The practical section contains historical data on Iraq's annual …

Summary Supervised Machine Learning (SML) algorithms stands on the principle of
generating theories on the existing data instances to make predictions on the upcoming data …

Financial efficiency analysis: Empirical evidence from the emerging stock market Page 1
Corporate Law & Governance Review / Volume 4, Issue 2, 2022 27 FINANCIAL EFFICIENCY …