4 skills found
larasupernovae / Nmpc Flash Multi ObstacleRepository for Enhanced Path Planning and Nonlinear Model Predictive Control (NMPC) for DJI UAVs. Features robust obstacle avoidance, lidar distance from the wall, complex trajectory tracking (B-spline) and ROS integration on DJI Matrice 100. Includes simulation code, real-world testing results, and documentation.
mwheymans / Psfmipsfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets
Daniblit / Ensemble Predictive Model Forecasting AMGEN Stock Price At Year End 31sThe basis of this project involves analyzing Amgen future profitability based on its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. The dataset used for this analysis was downloaded from Yahoo finance for year 2009 to 2019. There are multiple variables in the dataset – date, open, high, low, volume. Adjusted close. The columns Open and Close represent the starting and final price at which the stock is traded on a day. High and Low represent the maximum, minimum price of the share for the day. The profit or loss calculation is usually determined by the closing price of a stock for the day, I used the adjusted closing price as the target variable. I downloaded data on the inflation rate, unemployment rate, Industrial Production Index, Consumer Price Index for All Urban Consumers: All Items and Real Gross Domestic Product as independent variables, Quarterly Financial Report: U.S. Corporations: Cash Dividends Charged to Retained Earnings All Manufacturing: All Nondurable Manufacturing: Chemicals: Pharmaceuticals and Medicines Industry, Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing, 30-Year Treasury Constant Maturity Rate, and Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing Index. The independent variables are economic parameters which was obtained from Federal Reserve Economic Data (FRED) website. Methodology 1. Linear Regression: The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable. I used linear regression tool in Alteryx with ARIMA tool to forecast the stock prices for the year. The algorithm was trained with the historical data to see how the variables impact on the dependent variable. The test data was used to predict the adjusted closing price for the year and predicted a stock price of $193.38. 2. Support Vector Machines (SVM): Support Vector Networks (SVN), are a popular set of supervised learning algorithms originally developed for classification (categorical target) problems and can be used for regression (numerical target) problems. SVMs are memory efficient and can address many predictor variables. This model finds the best equation of one predictor, a plane (two predictors) or a hyperplane (three or more predictors) that maximally separates the groups of records, based on a measure of distance into different groups based on the target variable. A kernel function provides the measure of distance that causes to records to be placed in the same or different groups and involves taking a function of the predictor variables to define the distance metric. I used the SVM tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $189.44. 3. Spline Model: The Spline Model tool was used because it provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman. This statistical learning model self-determines which subset of fields best predict a target field of interest and can capture highly nonlinear relationships and interactions between fields. I used the Spline tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $201.84. The results from the models was weighted by comparing the RMSE of each model. A lower RMSE indicates that the model’s predictions were closer to the actual values. However, a simpler model with the same RMSE as a more complex model is generally better, as simpler models are less likely to be overfit. Though the Spline model had a lower RMSE, the Linear Regression model had fewer variables. Thus, we combined the 3 models with the ARIMA forecast in a model ensemble, which allows us to use the results of multiple models. The forecasted stock price is $197.99 with 1.5% increase for 31st December 2019. Apart from economic parameters, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors which can often be impossible to predict beforehand hence the model predicts that the stock price of Amgen will continue to rise except there is a drastic downturn of the company.
YuyangLi0606 / Dynamic IVSThe coding supplemental material for paper "Modelling Implied Volatility Surface Using B-splines with Time-dependent Coefficients Predicted by Tree-based Machine Learning Methods"
