110 skills found · Page 1 of 4
scikit-learn-contrib / Py EarthA Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines
chhayac / Machine Learning Notebooks15+ Machine/Deep Learning Projects in Ipython Notebooks
const-ae / LemurLatent Embedding Multivariate Regression
Magica-Chen / Gptp Multi Outputmultivariate Gaussian process regression and multivariate Student-t process regression
ludobouan / Linear Regression SklearnMultivariate linear regression with sklearn
elcorto / Pwtoolspwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.
sapphire921 / Midas ProPython version of Mixed Data Sampling (MIDAS) regression (allow for multivariate MIDAS) :golf:
benman1 / Time SeriesTime-Series models for multivariate and multistep forecasting, regression, and classification
mrocklin / MultipolyfitA multivariate polynomial regression function in python
reddyprasade / Machine Learning With Scikit Learn Python 3.xIn general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
gabrielegilardi / ANFISMultivariate Regression and Classification Using an Adaptive Neuro-Fuzzy Inference System (Takagi-Sugeno) and Particle Swarm Optimization.
sigvaldm / LocalregMultivariate Local Polynomial Regression and Radial Basis Function Regression
eliotwalt / Gaf CnnMultivariate timeseries to multivariate timeseries convolution regressor based on the article "Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks" by Wang, Z.; Oates, T. in Proceedings of the Workshops at AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January–1 February 2019, pp. 40–46.
yitaohu88 / Empirical Method In FinanceWinter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices Class intro: Forecasting and Finance The random walk hypothesis Stationarity Time-varying volatility and General Least Squares Robust standard errors and OLS Topic 2: Time-dependence and predictability ARMA models The likelihood function, exact and conditional likelihood estimation Predictive regressions, autocorrelation robust standard errors The Campbell-Shiller decomposition Present value restrictions Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity Time-varying volatility in the data Realized Variance ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns Single- and multifactor models Economic factors: Models and data exploration Statistical factors: Principal Components Analysis Fama-MacBeth regressions and characteristics-based factors
andreachello / Applied Econometric Time SeriesA repository to explore the concepts of applied econometrics in the context of financial time-series.
pavelkomarov / Projection PursuitAn implementation of multivariate projection pursuit regression and univariate classification
tab114 / Marketing Mixed Modelling AnalysisFitted a multivariate regression model on a brand’s product Sales Volume and the availabe marketing time series data to (i.e. Advertising, Distribution, Pricing) to estimate the impact of various marketing tactics on sales and then forecast the impact of future sets of tactics.
StefanBloemheuvel / GCNTimeseriesRegressionGithub page for: Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data
patr1ckm / MvtboostBoosted regression trees for multivariate, longitudinal, and hierarchically clustered data.
mljs / Regression Multivariate LinearMultivariate linear regression
