Kindly share this postAs part of activities leading up to her investiture as the 14th President of the Chartered Institute of ...

Kindly share this postIfeanyi Nwanegbo, a distinguished Data Scientist and Applied Data Science professional, has been ...

Abstract: India’s rapid urbanization is driving energy demand to new heights, increasing dependence on nonrenewable resources and creating critical sustainability challenges for urban areas. This ...

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.

End-to-end demand forecasting with Python using synthetic time-series sales data. Includes data generation, cleaning, ARIMA/SARIMA model selection by AIC, evaluation with RMSE and MAPE, and 90-day ...

Accurate demand forecasting is essential for informed decision-making in today’s dynamic business environment, where product demand often follows diverse and shifting patterns throughout increasingly ...

Objectives To project the future burden of cancer mortality in India by forecasting age-standardised mortality rates (ASMRs) for 23 major cancer types up to the year 2030, providing crucial evidence ...

Have you ever imagined predicting your company’s revenue for the coming months based on past data? This is the power of time series forecasting, which analyzes data organized chronologically, such as ...

Abstract: Sales forecasting plays a pivotal role in predicting future revenue by estimating product or service volumes within a specified time frame. Accurate sales forecasting drives strategic ...

In this study, we address the challenge of accurate time series forecasting of air passenger demand using historical market demand data from the U.S. commercial aviation industry in the 21st century.