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16 articles

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Jul 4, 2025 · Operations

How Dynamic Thresholds with Prophet Transform Monitoring from Static Alerts to Intelligent Insights

Traditional fixed‑threshold monitoring often triggers noisy alerts during routine business rhythms, but by modeling time‑series patterns with Facebook Prophet to predict dynamic confidence intervals, teams can automatically adjust thresholds, reduce false positives, and accurately detect true anomalies across diverse services.

ProphetTime Seriesanomaly detection

0 likes · 7 min read

How Dynamic Thresholds with Prophet Transform Monitoring from Static Alerts to Intelligent Insights

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.

AutoformerProphetTimesNet

0 likes · 18 min read

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

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.

ProphetPythonTime Series

0 likes · 11 min read

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

Network Intelligence Research Center (NIRC)

Jul 1, 2023 · Artificial Intelligence

Prompting Large Language Models for Knowledge‑Based Visual Question Answering: The Prophet Framework

This article analyzes the Prophet framework, which leverages a traditional VQA model to generate answer candidates and in‑context examples that prompt GPT‑3, achieving state‑of‑the‑art performance on the challenging OK‑VQA and A‑OKVQA benchmarks.

GPT-3MCANPrompt engineering

0 likes · 9 min read

Prompting Large Language Models for Knowledge‑Based Visual Question Answering: The Prophet Framework

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

Alibaba Cloud Developer

May 17, 2022 · Artificial Intelligence

How Databricks and Prophet Power Retail Demand Forecasting for Store‑Item Sales

This article walks through why accurate demand forecasting is critical for retailers, shows how to prepare and visualize sales data, demonstrates building a store‑item model with Databricks DDI and Facebook Prophet, and explains scaling the model to predict every product across all stores, highlighting performance metrics and practical tips.

DatabricksProphetSpark

0 likes · 7 min read

How Databricks and Prophet Power Retail Demand Forecasting for Store‑Item Sales

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.

DBSCANFFTProphet

0 likes · 10 min read

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

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.

DBSCANFFTProphet

0 likes · 10 min read

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

GuanYuan Data Tech Team

Apr 14, 2022 · Artificial Intelligence

Mastering Time Series Forecasting: From Moving Averages to Transformers

Time series forecasting, essential across weather, finance, and commerce, involves tasks like classification, clustering, anomaly detection, and especially prediction; this article explores its definitions, evaluation metrics, traditional methods, machine‑learning approaches, deep‑learning models such as TFT, and emerging AutoML tools, offering practical insights and best practices.

AutoMLDeep LearningGBDT

0 likes · 27 min read

Mastering Time Series Forecasting: From Moving Averages to Transformers

Python Programming Learning Circle

Jan 14, 2022 · Artificial Intelligence

Time Series Sampling, Resampling, Prophet Modeling, LSTM Forecasting, and Clustering with Python

This tutorial explains how to work with time‑series data in Python, covering dataset preparation, resampling techniques, Prophet forecasting, LSTM‑based prediction, and hierarchical clustering, while illustrating each step with visual examples and code snippets.

LSTMProphetPython

0 likes · 6 min read

Time Series Sampling, Resampling, Prophet Modeling, LSTM Forecasting, and Clustering with Python

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.

Brier scoreKPIProphet

0 likes · 13 min read

KPI Forecasting and Anomaly Detection at Tubi Using Prophet

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.

Parameter TuningProphetPython

0 likes · 14 min read

Prophet Parameter Tuning and Practical Guide for Time Series Forecasting

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.

LSTMProphetTime Series

0 likes · 16 min read

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

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.

ARIMAHolt-WintersLSTM

0 likes · 12 min read

Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring

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

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 ModelData ScienceDatabase Storage Prediction

0 likes · 5 min read