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.

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 ...

To make a better explanation of ARIMA we can also write it as (AR, I, MA) and by this, we can assume that in the ARIMA, p is AR, d is I and q is MA. ARIMA models integrate Auto Regression, Moving ...

Exponential smoothing and moving average are key techniques for time series forecasting. Exponential smoothing assigns exponentially decreasing weights to observations over time. Moving average ...