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Ctrip Technology
Ctrip Technology
Oct 26, 2023 · Artificial Intelligence

Time Series Forecasting of Key Business Indicators: Methods, Models, and Practical Deployment

This article presents a comprehensive study on forecasting critical business metrics such as traffic, order volume, and GMV using traditional, machine‑learning, and deep‑learning time‑series models, detailing feature engineering, model design, experimental comparison, online deployment, and monitoring strategies.

AutoformerInformerProphet
0 likes · 18 min read
Time Series Forecasting of Key Business Indicators: Methods, Models, and Practical Deployment
Model Perspective
Model Perspective
Sep 9, 2023 · Fundamentals

Cleaning, Interpolating, and Forecasting Yellow River Sediment Data with Python

This article walks through cleaning missing hydrological data, applying various interpolation techniques, visualizing water level, flow, and sediment concentration, decomposing the time series into trend, seasonal, and residual components, and using Prophet to forecast future river sediment levels.

ProphetPythondata cleaning
0 likes · 11 min read
Cleaning, Interpolating, and Forecasting Yellow River Sediment Data with Python
Model Perspective
Model Perspective
Aug 23, 2022 · Fundamentals

How Prophet Implements Time Series Decomposition and Trend Modeling

This article explains Prophet’s algorithmic approach to time‑series forecasting, covering decomposition into trend, seasonality, holidays and error components, logistic and piecewise linear trend models, automatic change‑point detection, Fourier‑based seasonality, holiday handling, model fitting with PyStan, and practical Python code examples.

ProphetPythonholiday effects
0 likes · 12 min read
How Prophet Implements Time Series Decomposition and Trend Modeling
DataFunSummit
DataFunSummit
May 8, 2022 · Artificial Intelligence

Machine Learning‑Based Time Series Forecasting and Anomaly Detection System at JD Search

The article describes JD Search's machine‑learning alert system that combines offline and real‑time training, FFT‑based periodic detection, Prophet forecasting, and DBSCAN anomaly clustering, and explains architectural design, data preprocessing, model optimization, and distributed deployment to improve alert accuracy and response speed.

Anomaly DetectionDBSCANFFT
0 likes · 10 min read
Machine Learning‑Based Time Series Forecasting and Anomaly Detection System at JD Search
DataFunTalk
DataFunTalk
Apr 24, 2022 · Artificial Intelligence

Machine Learning‑Driven Time Series Forecasting and Anomaly Detection System at JD Search

The article describes JD Search’s machine‑learning‑based time‑series forecasting and anomaly‑detection platform, detailing its overall architecture, offline and real‑time training pipelines, FFT‑based periodicity detection, Prophet forecasting, DBSCAN outlier detection, and distributed optimizations such as Alink integration and load‑balancing strategies.

Anomaly DetectionDBSCANFFT
0 likes · 10 min read
Machine Learning‑Driven Time Series Forecasting and Anomaly Detection System at JD Search
Bitu Technology
Bitu Technology
Aug 28, 2020 · Artificial Intelligence

KPI Forecasting and Anomaly Detection at Tubi Using Prophet

This article describes how Tubi’s data science team built a robust KPI forecasting system with Facebook’s Prophet, covering visualization dashboards, anomaly detection, feature engineering, PySpark deployment, and evaluation using Brier scores to improve business decision‑making.

Anomaly DetectionBrier scoreKPI
0 likes · 13 min read
KPI Forecasting and Anomaly Detection at Tubi Using Prophet
Fulu Network R&D Team
Fulu Network R&D Team
Jul 21, 2020 · Artificial Intelligence

Prophet Parameter Tuning and Practical Guide for Time Series Forecasting

This article provides a comprehensive tutorial on Prophet's key parameters, their meanings, and practical tips for tuning them—including growth, changepoints, seasonalities, holidays, and Bayesian settings—along with Python code examples for grid search and cross‑validation to improve forecasting accuracy.

ProphetPythoncross-validation
0 likes · 14 min read
Prophet Parameter Tuning and Practical Guide for Time Series Forecasting
Fulu Network R&D Team
Fulu Network R&D Team
Jun 11, 2020 · Artificial Intelligence

Intelligent Inventory Management: Comparing Prophet and LSTM for Time‑Series Forecasting

This article presents an intelligent inventory management solution that predicts product consumption using two time‑series algorithms—Facebook's Prophet and LSTM deep learning—detailing data sources, preprocessing, model configuration, evaluation metrics, and a comparative analysis of their performance and suitability.

LSTMMachine LearningProphet
0 likes · 16 min read
Intelligent Inventory Management: Comparing Prophet and LSTM for Time‑Series Forecasting
NetEase Game Operations Platform
NetEase Game Operations Platform
Dec 21, 2019 · Artificial Intelligence

Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring

The article reviews traditional, neural network, and open‑source time‑series forecasting methods, explains their strengths and limitations, and demonstrates how NetEase applies short‑term and long‑term prediction models such as Holt‑Winters, ARIMA, STL, Prophet, and LSTM to improve game monitoring and proactive alerting.

AIOpsARIMAHolt-Winters
0 likes · 12 min read
Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring
Ctrip Technology
Ctrip Technology
Aug 7, 2018 · Artificial Intelligence

Forecasting and Monitoring in Business Intelligence: Practical Data‑Analysis Methods and Model‑Building Tips

The article explains how a data analyst can use statistical and machine‑learning models such as linear regression, tree‑based boosting, STL decomposition, and Prophet for both non‑time‑series forecasting and time‑series monitoring, highlighting data‑quality concerns, feature‑engineering practices, and deployment considerations like PMML packaging.

BIProphetSTL
0 likes · 13 min read
Forecasting and Monitoring in Business Intelligence: Practical Data‑Analysis Methods and Model‑Building Tips
Tencent Cloud Developer
Tencent Cloud Developer
Jul 13, 2018 · Artificial Intelligence

Using Facebook Prophet for Time Series Forecasting: Predicting Tencent Cloud Database Storage Trends

The article explains Facebook Prophet’s additive regression model and demonstrates its use to forecast Tencent Cloud database storage demand, showing upward trends and growing uncertainty from January‑June 2018 data, while highlighting practical applications for internal customer identification and capacity planning.

Additive Regression ModelDatabase Storage PredictionFacebook Prophet
0 likes · 5 min read
Using Facebook Prophet for Time Series Forecasting: Predicting Tencent Cloud Database Storage Trends