Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti

L1 adaptive controller has proven to provide fast adaptation with guaranteed transients in a large variety of systems. It is commonly used for controlling systems with uncertain time-varying unknown parameters. The effectiveness of  L1 adaptive controller for position control of single axis has been examined and compared with Model Reference Adaptive Controller (MRAC). The Linear servo motor is one of the main constituting elements of the x-y table which is mostly used in automation application. It is characterized by time-varying friction and disturbance.

    The tracking and steady state performances of both controllers have been assessed fo

Twilight is that light appear on the horizon before sunrise and after sunset, Astronomically it is known that sunrise and sunset are effected by high above sea level, but the effect of high above sea level on the time of astronomical twilight still not decided and controversy among astronomers, in This research we studies the effect of high above sea level on the time of astronomical twilight, through adding the equation correct high above sea level to equation computation of twilight and then calculate of changing in the time of twilight for different highest (0-10000) meters above sea level , and the ratio of increase for time with high between (15.45-20.5) minutes. It was found that there was an increase in the time of the twilight along

        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 Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h

The estimation of the regular regression model requires several assumptions to be satisfied such as "linearity". One problem occurs by partitioning the regression curve into two (or more) parts and then joining them by threshold point(s). This situation is regarded as a linearity violation of regression. Therefore, the multiphase regression model is received increasing attention as an alternative approach which describes the changing of the behavior of the phenomenon through threshold point estimation. Maximum likelihood estimator "MLE" has been used in both model and threshold point estimations. However, MLE is not resistant against violations such as outliers' existence or in case of the heavy-tailed error distribution. The main goal of t

Abstract

In this research we study the wavelet characteristics for the important time series known as Sunspot, on the aim of verifying the periodogram that other researchers had reached by the spectral transform, and noticing the variation in the period length on one side and the shifting on another.

A continuous wavelet analysis is done for this series and the periodogram in it is marked primarily. for more accuracy, the series is partitioned to its the approximate and the details components to five levels, filtering these components by using fixed threshold on one time and independent threshold on another, finding the noise series which represents the difference between