Current Python alternatives for statistical models are slow, inaccurate and don't scale well. So we created a library that can be used to forecast in production environments or as benchmarks.

This important study presents compelling observational data supporting a role for transcription and polysome accumulation in the separation of newly replicated bacterial chromosomes. The study is ...

The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model ...

ARIMA models integrate Auto Regression, Moving Average, and differencing to analyse non-stationary time series. Identifying the optimal parameters p, d, and q is crucial for effective time series ...

In time series exponential smoothing can be considered as a method to smooth the time series data. We can also consider it as a thumb rule technique which is an approximate method of doing something.