Tomato production is a crucial component of the agricultural sector in Asian countries. Accurate forecasting of tomato production is essential for effective agricultural planning, resource allocation, ...

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 annual incidence, mortality, and DALYs rates for lip and oral cavity neoplasms were estimated using data from the GBD 2021 study. Data were obtained from 1990 to 2021 from the publicly available ...

Forecasting is the art and science of predicting future events. It is a critical tool for businesses, allowing them to make informed decisions across various domains, including supply chain management ...

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 ...

Time series forecasting is a fundamental task in data science, applied statistics, and econometrics. With time series forecasting we aim to predict the future values of time series datasets. A time ...

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.