Tag：variance
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Deviation and variance of ensemble learning
Introduction: We often use fitting and under fitting to qualitatively describe whether the model solves specific problems well. From a quantitative point of view, the bias and variance of the model can be used to describe the performance of the model. Ensemble learning can “magically” improve the performance of weak classifiers. 1、 How did the […]
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R language – Analysis of variance and regression analysis
this article isLiu Qiang, Pei Yanbo, Zhang Beibei R language and modern statistical methodsThe reading record of a book has only rough operation steps. People without basic mathematical statistics and relevant mathematical knowledge should use this book and this article with caution. 1、 Return fit <- lm(formula, data=data) The formula part can be y […]
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[mindspire: machine learning with little MI] some suggestions on applying machine learning
So far, little MI has introduced many different learning algorithms to you. I feel that everyone has unconsciously become an expert who knows many advanced machine learning technologies. Of course, how to use these learning algorithms efficiently and effectively while understanding? How to choose the most appropriate and correct path instead of wasting time on […]
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Detailed explanation of BN core parameters of pytorch
Detailed explanation of BN core parameters of pytorch Original document:https://www.yuque.com/lart/ug… BN is a common operation and module in CNN. In the specific implementation, it contains multiple parameters. This also leads to different effects under different parameter combinations. affine Modify during initialization When fine is set to true, the batchnorm layer will learn the parameters gamma […]
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R language penalty logistic regression (Lasso, ridge regression) classification model case of high-dimensional variable selection
Original link:http://tecdat.cn/?p=21444 Logistic regression is a commonly used method in research, which can screen influencing factors, predict probability, classify, etc. for example, the data obtained by Qualcomm sequencing technology in medical research brings challenges to the selection of high-dimensional variables. Penalty logistic regression can select variables and estimate coefficients for high-dimensional data, and its effective […]
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Matlab uses GARCH model to fit and predict the fluctuation of stock market return time series
Original link:http://tecdat.cn/?p=24211 describe UsegarchSpecify a univariate GARCH (generalized autoregressive conditional heteroscedasticity) model. garchKey parameters of the model include: GARCH polynomial, which is composed of lag conditional variance. Order_ P_ Indicates. Arch polynomial, composed of lag square. Order_ Q_ Indicates. _ P_ And_ Q_ They are the maximum non-zero lag in GARCH and arch polynomials, respectively. […]
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Prediction of air quality ozone data with linear regression model in R language
Original link:http://tecdat.cn/?p=11387 Although linear models are one of the simplest machine learning techniques, they are still powerful tools for prediction. This is especially because linear models are particularly easy to explain this fact. Here, I will discuss the most important aspects of interpreting linear models using ordinary least squares regression examples of air quality data […]
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Test method of difference analysis
preface As we all know, you can’t boast when you are better this year than last year. Please put out the frequency and quality of your posts on developepper! Facing the data of this year and last year, maybe you need a statistical test method Inter group difference test, finally someone made it clear! summary […]
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R language arma-egarch model and integrated prediction algorithm predict the actual volatility of SPX
Original link:http://tecdat.cn/?p=12174 – introduce This paper compares several time series models to predict the daily performance of SP500 indexActual volatility。 The benchmark is the arma-egarch model of SPX daily return series. Compare it with GARCH model. Finally, a set prediction algorithm is proposed. Hypothetical conditions The actual volatility is invisible, so we can only estimate […]
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R language value at risk: Arima, GARCH, delta normal method, rolling estimation of VaR (value at risk) and back test analysis of stock data
Original link: http://tecdat.cn/?p=24492 introduce The purpose of this analysis is to construct a process to correctly estimate var under the condition of variable volatility at a given time. Value at risk is widely used to measure the market risk of financial institutions. Our time series data include 1258 days of stock returns. In order to […]
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Analysis of stock market volatility by GARCH model of R language time series
Original link:http://tecdat.cn/?p=22360 In this article, we will learn a standard method to establish volatility model in price series, namely generalized autoregressive conditional heteroscedasticity (GARCH) model. The idea of GARCH model of price fluctuation is to use the recent realization of error structure to predict the future realization of error structure. More simply, we often see […]