Statsmodels speed. 1. The dependent variable. For datasets with 100,000+ observations, you might notice slower fitting times compared to scikit-learn’s optimized implementations. 0 for extremely high accuracy. Jun 19, 2018 · I am searching for a way to speed up model training since we need to train many of them (i. An intercept is 20x faster than pmdarima. The statsmodels ols) method is used on a cars dataset to fit a multiple regression model using Quality as the response variable. To profile things up, I took an example model, which takes 190 seconds to train. random. normal(size=nsample). Replace FB-Prophet in two lines of code and gain speed and accuracy. OLS class statsmodels. 500x faster than Prophet. Jan 29, 2025 · Use techniques like parallel processing or optimized algorithms to speed up your analysis. FFT is extremely fast, but only works on periodic data. statsmodels also has some built-in optimizations, but you can further enhance performance by choosing the right data structures and algorithms. minimize interface to L-BFGS-B. Nov 19, 2025 · Statsmodels prioritizes statistical correctness over computational speed. While statsmodels works well with small and moderately-sized data sets that can be loaded in memory–perhaps tens of thousands of observations–use cases exist with millions of observations or more. Depending on the model and the data, choosing an appropriate scipy optimizer enables avoidance of a local minima, fitting models in less time, or fitting a model with less memory. 8, 3. column_stack((x, x ** 2)) beta = np. linear_model. 5x faster than R. OLS(endog, exog=None, missing='none', hasconst=None, **kwargs) [source] Ordinary Least Squares Parameters endog : array_like A 1-d endogenous response variable. This is the recommended installation method for most users. Fit 10 benchmark models on 1,000,000 series in under 5 min. See Notes for relationship to ftol, which is exposed (instead of factr) by the scipy. kernel_regression. optimize. Speed and Angle are used as predictor variables. 1,000,000 series in 30 min with ray. KernelReg Asked 6 years, 3 months ago Modified 6 years, 3 months ago Viewed 1k times Nov 23, 2021 · Under the hood both statsmodels and sklearn rely on Moore-Penrose pseudoinverse and can invert singular matrices just fine, the problem is that the coefficients obtained in the singular covariance matrix case don't mean anything in any physical sense. There are likely just a few usecases inside statsmodels where GPU might help. (with the caveat that I don't know much about use cases for GPU) Savgol is a middle ground on speed and can produce both jumpy and smooth outputs, depending on the grade of the polynomial. Setup I generated 1000 data points in the shape of a sin curve: The ols () method in statsmodels module is used to fit a multiple regression model using “Quality” as the response variable and “Speed” and “Angle” as the predictor variables. regression. Jan 21, 2020 · For numba speed-up we can just optimize a single function in the center of a model or a stats function. Check the experiments here. linspace(0, 10, 100) X = np. Maximum number of iterations. As a consequence, the emphasis in the supporting features of statsmodels is in analysing the training data which includes hypothesis tests and goodness-of-fit measures, while the emphasis in the supporting infrastructure in scikit-learn is on model selection for out-of-sample prediction and therefore cross-validation on "test data". exog : array_like A nobs x k array where nobs is the number of observations and k is the number of regressors. 9 Dec 5, 2025 · nsample = 100 x = np. dozens of millions). nonparametric. 4x faster than statsmodels. Nov 13, 2019 · Any way to speed up initialization of statsmodels. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10. Depending on the model and the data, choosing an appropriate scipy optimizer enables avoidance of a local minima, fitting models in less time, or fitting a model with less memory. 1, 10]) e = np. statsmodels supports the following optimizers along with keyword arguments associated with that specific optimizer: What I find is that model fitting during the grid search takes a very long time, about 11 minutes, for a couple of agents, whereas the same 48 iterations take much less time, less than 10 seconds, for others. Instructions for installing from PyPI, source or a development version are also provided. Moving average methods with numpy are faster but obviously produce a graph with steps in it. array([1, 0. statsmodels supports the following optimizers along with keyword arguments associated with that specific optimizer: statsmodels. Missing something? Please open an issue or write us in Installing statsmodels The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. e. Python Support statsmodels supports Python 3. bkruwx rvskl mau injnar boweq gtyw rrds sucvoo xwrl vduawa