GitHub

Store Sales - Time Series Forecasting

This end-to-end project aims to forecast retail store sales based on historical data, helping businesses optimize inventory management, reduce waste, and improve revenue planning. It includes data ...
GitHub

Forecasting with time series imaging

This page introduces how to use our code for image-based time series forecasting. The code is divided 2 parts: feature extraction with sift or pretrained CNN model combination based on the extracted ...
Nature

Forecasting tomato production in major Asian producers: a comparative study of ARIMA, exponential smoothing, score-driven models, and XGBoost

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, ...
Frontiers

Forecasting the fracture: a cost-driven machine learning framework for optimal bridge maintenance prioritization

Aging civil infrastructure presents a critical economic and public safety challenge, with maintenance backlogs costing hundreds of billions of dollars. This study moves beyond simple condition ...
VentureBeat

Beyond the lakehouse: Fundamental's NEXUS bypasses manual ETL with a native foundation model for tabular data

The deep learning revolution has a curious blind spot: the spreadsheet. While Large Language Models (LLMs) have mastered the nuances of human prose and image generators have conquered the digital ...
Nature

Real-time emission prediction for ship engine using stacked time-series learning: a transformer-XGBoost hybrid framework

According to the latest Review of Maritime Transport by united nations conference on trade and development (UNCTAD), international seaborne trade reached 12,292 million tons in 2023, representing a ...
LinkedIn

Prophet vs XGBoost: Who forecasts Walmart's Holiday Sales Better?

In this follow-up article, we dive deeper into two time series forecasting models: Prophet (an additive time series forecasting model designed for time-dependent data) and XGBoost (an efficient, ...
Analytics Insight

Best Python Libraries for Machine Learning in 2025

Scikit-learn, PyTorch, and TensorFlow remain core tools for structured data and deep learning tasks. New libraries like JAX, Polars, and LangChain offer speed, scalability, and real-time ML ...
Scientific Research Publishing

Extra Trees Model for Heart Disease Prediction ()

1 Computer Science Department, Babcock University, Ilishan-Remo, Ogun State, Nigeria. 2 Computer Science Department, Adeleke University, Ede, Osun State, Nigeria. 3 Department of Applied Mathematics, ...
Frontiers

Explainable machine learning to predict the cost of capital

This study investigates the impact of financial and non-financial factors on a firm's ex-ante cost of capital, which is the reflection of investors' perception on a firm's riskiness. Departing from ...
LinkedIn

Mastering XGBoost: From Basics to Advanced Techniques with a Complete Use Case

In today's world, data is everywhere, and Machine Learning (ML) has become an essential tool to make sense of it. One of the most popular and powerful ML algorithms is XGBoost, short for eXtreme ...