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

The study explores the effectiveness of the ARIMA(3,1,3) model in predicting market trends, specifically accounting for macroeconomic shifts like the 2026 CPI base year updates. Stationarity ...

A comprehensive and professional Python implementation of the X13-ARIMA-SEATS seasonal adjustment algorithm. This library provides robust, production-ready tools for detecting and removing seasonal ...

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 ...

1 Information System Department, Faculty of Commerce and Business Administration Helwan University, Cairo, Egypt. 2 Computer Science Department, Faculty of Computer and Artificial Intelligence, Helwan ...

Mine water influx is a significant geological hazard during mine development, influenced by various factors such as geological conditions, hydrology, climate, and mining techniques. This phenomenon is ...

Applying ARIMA (AutoRegressive Integrated Moving Average) directly in Excel can be a bit complex because Excel doesn't have built-in functions specifically for ARIMA modeling. However, you can still ...

With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning ...

Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts ...