This article serves to familiarize the reader with the empirical mode decomposition (EMD) method. It is the fundamental part of the Hilbert–Huang transform and is intended for analyzing data from nonstationary and nonlinear processes. This article also features a possible software implementation of this method along with a brief consideration of its peculiarities and gives some simple examples of its use.
The article deals with the creation of a program allowing to estimate the kernel density of the unknown probability density function. Kernel Density Estimation method has been chosen for executing the task. The article contains source codes of the method software implementation, examples of its use and illustrations.
The article is intended to get its readers acquainted with the Box-Cox transformation. The issues concerning its usage are addressed and some examples are given allowing to evaluate the transformation efficiency with random sequences and real quotes.
This article seeks to upgrade the indicator created earlier on and briefly deals with a method for estimating forecast confidence intervals using bootstrapping and quantiles. As a result, we will get the forecast indicator and scripts to be used for estimation of the forecast accuracy.
The article familiarizes the reader with exponential smoothing models used for short-term forecasting of time series. In addition, it touches upon the issues related to optimization and estimation of the forecast results and provides a few examples of scripts and indicators. This article will be useful as a first acquaintance with principles of forecasting on the basis of exponential smoothing models.
This article introduces a class designed to give a quick preliminary estimate of characteristics of various time series. As this takes place, statistical parameters and autocorrelation function are estimated, a spectral estimation of time series is carried out and a histogram is built.
Estimation of statistical parameters of a sequence is very important, since most of mathematical models and methods are based on different assumptions. For example, normality of distribution law or dispersion value, or other parameters. Thus, when analyzing and forecasting of time series we need a simple and convenient tool that allows quickly and clearly estimating the main statistical parameters. The article shortly describes the simplest statistical parameters of a random sequence and several methods of its visual analysis. It offers the implementation of these methods in MQL5 and the methods of visualization of the result of calculations using the Gnuplot application.
Today it is difficult to find a computer that does not have an installed web-browser. For a long time browsers have been evolving and improving. This article discusses the simple and safe way to create of charts and diagrams, based on the the information, obtained from MetaTrader 5 client terminal for displaying them in the browser.
This article is intended to get its readers acquainted with a possible variant of using graphical objects of the MQL5 language. It analyses an indicator, which implements a panel of managing a simple spectrum analyzer using the graphical objects. The article is meant for readers acquianted with basics of MQL5.
This article briefly describes the author's opinion on redrawing indicators, multi-timeframe indicators and displaying of quotes with Japanese candlesticks. The article contain no programming specifics and is of a general character.