我们的探索性数据分析（EDA）揭示了对酒店预订趋势的关键见解。通过时间序列分析，我们揭示了预订高峰期和季节变化。这些发现为战略决策提供了信息，提高了酒店业的客户满意度。
Our Exploratory Data Analysis (EDA) reveals crucial insights into hotel booking trends. Through time series analysis, we uncover peak booking periods and seasonal variations. These findings inform strategic decisions, enhancing customer satisfaction in the hospitality industry. (2024-05-12, Jupyter Notebook, 0KB, 下载0次)
：感叹号：这是CRAN R软件包存储库的只读镜像。STMotif-在空间时间序列中发现基序。主页：https:github.com先驱博尔赫斯STMotif wiki报告此软件包的错误：https:github.com先锋博尔赫斯·STMotiv问题
:exclamation: This is a read-only mirror of the CRAN R package repository. STMotif — Discovery of Motifs in Spatial-Time Series. Homepage: https: github.com heraldoborges STMotif wiki Report bugs for this package: https: github.com heraldoborges STMotif issues (2024-02-25, R, 0KB, 下载0次)
加州酒店业员工的季节性时间序列
A seasonal time series of California Hospitality Industry Employees (2024-02-01, Jupyter Notebook, 0KB, 下载0次)
Kaggle Competition“软件缺陷数据集的二进制分类”游乐场系列-第3季，1702年利德尔博23288集...
Kaggle Competition "Binary Classification with a Software Defects Dataset" Playground Series - Season 3, Episode 23 288 from 1702 Liderbord position. This laptop skips calculations of individual cells, which require a long time, and immediately inserts the results obtained from calculations using other resources. (2023-12-05, Jupyter Notebook, 0KB, 下载0次)
基于React的AI酒店预订仪表板，具有Typescript集成。功能包括日期范围选择器、时间序列图、国家/地区...
React-based AI Hotel Booking Dashboard with Typescript integration. Features include date range selector, time series chart, country-wise distribution, and sparkline charts for adult and children visitors. Interactive and insightful analysis of 1,000 hotel booking records. (2023-11-09, TypeScript, 0KB, 下载0次)
2023斯托尔博夫时间序列课程，，
2023 Stolbov TimeSeriesCourse,, (2023-09-24, Others, 0KB, 下载0次)
GDTW是一个Python C++库，用于执行动态时间扭曲。它是根据戴夫·德里索和斯蒂芬·博伊德的一篇论文编写的。
GDTW is a Python C++ library that performs dynamic time warping. It is based on a paper by Dave Deriso and Stephen Boyd. (2023-04-09, Jupyter Notebook, 30117KB, 下载0次)
用机器学习预测酒店取消和ADR。分类、回归、时间序列分析。
Predicting Hotel Cancellations and ADR with machine learning. Classification, regression, time series analysis. (2023-04-18, Jupyter Notebook, 8191KB, 下载0次)