This is part of a series of methods to predict stock performance by using different statistic approaches Question: How can I predict the performance of a stock or time series process using the distribution of the data? Solution: For this we will use a stock at random like VISA with ticker Vin the NYSE. To use this predictive method we calculate the density (probability) of the different daily returns and we calculate several paths that the stock could follow.

Question: How can I fit a sigmoid function to time series data in R? Because when I run nls I get the following error Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates Solution: Normally we find many problems when trying to fit a non linear model to time series data. Specifically, when we don’t define correctly the starting values we get the error of singular gradient matrix error.

Linear Regression We first simulate data to see how to see how to calculate beta (slope) and alpha (constant) coefficients. We use a sampling method from a multivariate normal distribution with mean [0,0] and some covariance between independent variable x and target variable y. # Import Libraries import numpy as np import matplotlib.pyplot as plt m = range(0,10000) # Datasets n = 100 # number of points sigx = .

Question: I’m not a math expert. But I want to understand least squares completely. Can you please explain how to do Least Squares Regression from Scratch? Preface Regression is at the core of Machine Learning. Almost all the literature in predictive modelling, time series forecasting, and Artificial Intelligence, relies on the basic principles of linear regression. Nowadays everyone can compute linear regression using statistical software or even Excel, but how many people really know what’s going on behind the scenes.

Question: I’m working on a regression model and I need to force the regression coefficients to be positive. How can I accomplish this in R? Solution: For linear model regression model with restricted coefficients you have 3 options: Linear with nls, Bayes with brms and Lasso. Here we will look at Linear Model with nls The function lm does not provide a way to restrict coefficients. Instead we can use the function nls under the algorithm port.

Question: I’m working on a regression model and I need to force the regression coefficients to be positive. How can I accomplish this in R? Solution: For linear model regression model with restricted coefficients you have 3 options: Linear with nls, Bayes with brms and Lasso. Here we will look at Linear Model with Lasso using glmnet In this case glmnet provides a convenient way to restrict coefficients regularizing the coefficients.

Question: I’m working on a regression model and I need to force the regression coefficients to be positive. How can I accomplish this in R? Solution: For linear model regression model with restricted coefficients you have 3 options: Linear with nls, Bayes with brms and Lasso. Here we will look at Linear Model with bayes regresison with the brms package In this case bayes provides a convenient way to restrict coefficients regularizing the coefficients.