Data Analysis

Hourly Natural Gas Load Forecasting Based on STL-XGBoost-NBEATSx

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Hourly Natural Gas Load Forecasting Based on STL-XGBoost-NBEATSx

1. Author Information Authors: Shao Bilin, Ren Meng, Tian Ning Affiliation: School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710311 Author Biography: Shao Bilin (1965-), male, professor, master’s supervisor, doctoral supervisor, research interests include big data, artificial intelligence, data information and management, energy sustainable development. E-mail: [email protected] Corresponding Author: Ren Meng (1999-), … Read more

LightGBM: A Gradient Boosting Tree Algorithm for Large-Scale Data

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LightGBM: A Gradient Boosting Tree Algorithm for Large-Scale Data

1 Algorithm Introduction LightGBM (Light Gradient Boosting Machine, hereinafter referred to as LGBM) is an efficient and scalable machine learning algorithm based on Gradient Boosted Decision Trees (GBDT). As a member of the GBDT framework algorithms and a successor to the XGB algorithm, LGBM effectively integrates a series of advantages from previous GBDT algorithms, including … Read more

Nutritional Component Analysis and Hypertension Prediction Based on XGBoost Model

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Nutritional Component Analysis and Hypertension Prediction Based on XGBoost Model

Click the blue text to follow us 2023, Issue 2 Nutritional Component Analysis and Hypertension Prediction Based on XGBoost Model Jiang Huai, Tan Lang, Li Shijie, Liu Yu, Wang Junfeng Abstract: Hypertension is a common chronic disease, and early detection and intervention can reduce the risk of complications. Although the onset and development of hypertension … Read more

Will XGBoost Algorithm Replace Linear Models?

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Will XGBoost Algorithm Replace Linear Models?

For most data analysts, regression models are undoubtedly one of the fundamental skills. At the beginning of this century, many investment banks used the application of regression models as a standard for evaluating analysts. For over a decade, regression models have maintained a significant presence in all analyses and predictions. However, in recent years, a … Read more

Monitoring Method for Ship Imitation Behavior Based on XGBoost

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Monitoring Method for Ship Imitation Behavior Based on XGBoost

Previous Review Recommended Article: Complexity of Xi’an Approach Airspace Based on Aircraft and Route Network Recommended Article: Lightweight Massive Spatiotemporal Data Processing and Analysis Service Framework This article was published in “Command Information Systems and Technology”, 2022, Issue 5 Authors:Sui Yuan,Duan Ran,Bai Zheng Citation Format:Sui Yuan, Duan Ran, Bai Zheng. Monitoring Method for Ship Imitation … Read more

Machine Learning and Bioinformatics: XGBoost Analysis

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Machine Learning and Bioinformatics: XGBoost Analysis

With the continuous development of genetics, breeding, and the increasing advancements in the Human Genome Project and molecular biology, biological data has experienced explosive growth over just a few decades. For example, algorithms such as regression analysis, random forests, and support vector machines in bioinformatics are already quite mature. Recently, while reading literature, I came … Read more

XGBoost Algorithm Implementation in Python

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XGBoost Algorithm Implementation in Python

Case Introduction This case aims to predict the Boston housing data using the XGBoost algorithm. The Boston housing dataset is a commonly used dataset for house price prediction, containing 506 samples and 13 features, including crime rates in the area, average number of rooms per dwelling, and distance to the city center. Algorithm Principle XGBoost … Read more

Integrated AHP and XGBoost Model for Food Safety Risk Prediction

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In recent years, China has made significant improvements in food quality and safety management. However, with the expansion of the food industry and the increasing demand for inspections, food safety testing data has exhibited high-dimensional, complex, and nonlinear characteristics. These features lead to low data utilization in quantitative analysis, directly affecting the accuracy of risk … Read more

Simple Time Series Prediction Using XGBoost (Python)

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Simple Time Series Prediction Using XGBoost (Python)

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import xgboost as xgb from sklearn.metrics import mean_squared_error color_pal = sns.color_palette() plt.style.use('fivethirtyeight') df = pd.read_csv('AAPL Hourly.csv') df = df[['timestamp', 'close']] df = df.set_index('timestamp') df.plot(style='.', figsize=(15, 5), color=color_pal[0], title='stock growth') plt.show() df.head() mean = df['close'].mean() mean 162.3033402402023 df.index = pd.to_datetime(df.index) … Read more

AI’s Innovative Applications in Multimodal Database Construction

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AI's Innovative Applications in Multimodal Database Construction

On May 10, 2024, an online and offline sharing event titled “AI’s Innovative Applications in Multimodal Database Construction” was held at the Tsinghua University MEM Center on Manufacturing Street in Zhongguancun. Despite the impending heavy rain, the event attracted more than twenty experts, scholars, and students from both China and abroad to participate offline, with … Read more