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DataFunTalk
DataFunTalk
Sep 8, 2022 · Databases

Elasticsearch as a Time Series Engine: Practices, Challenges, and Alibaba Cloud TimeStream Solutions

This article explains why Elasticsearch is being adapted as a time‑series engine, outlines its unique characteristics and challenges such as high query complexity and storage cost, and introduces Alibaba Cloud’s TimeStream solution with optimizations like index settings, compression, down‑sampling, and Prometheus integration.

DataStreamElasticsearchTime Series
0 likes · 13 min read
Elasticsearch as a Time Series Engine: Practices, Challenges, and Alibaba Cloud TimeStream Solutions
dbaplus Community
dbaplus Community
Sep 1, 2022 · Operations

How Vivo’s Server‑Side Monitoring Evolved: Architecture, Data Flow, and Alert Strategies

This article provides a comprehensive overview of Vivo's server‑side monitoring system, detailing its architecture evolution, data collection pipelines, OpenTSDB storage design, alerting mechanisms, and comparisons with other mainstream monitoring solutions, offering practical guidance for technology selection and implementation.

OpenTSDBOperationsSystem Architecture
0 likes · 18 min read
How Vivo’s Server‑Side Monitoring Evolved: Architecture, Data Flow, and Alert Strategies
Model Perspective
Model Perspective
Aug 24, 2022 · Fundamentals

How the GM(1,N) Grey Model Predicts Multi‑Variable Systems

The GM(1,N) prediction model extends the classic GM(1,1) approach to multiple indicator variables by applying accumulated generating operations, forming a first‑order differential equation, converting it to a discrete model, and using least‑squares estimation to derive prediction values for each variable.

Multivariate ForecastingPredictionTime Series
0 likes · 2 min read
How the GM(1,N) Grey Model Predicts Multi‑Variable Systems
Model Perspective
Model Perspective
Aug 11, 2022 · Fundamentals

Master VAR Modeling: Theory, Workflow, and Full Python Implementation

This guide explains the theory behind Vector Autoregression (VAR) models, outlines the complete modeling workflow—including data preparation, stationarity and cointegration testing, lag order selection, parameter estimation, stability diagnostics, and impulse‑response and variance‑decomposition analysis—and provides a full Python implementation with code examples.

ModelingPythonTime Series
0 likes · 9 min read
Master VAR Modeling: Theory, Workflow, and Full Python Implementation
Model Perspective
Model Perspective
Aug 9, 2022 · Fundamentals

How to Identify AR, MA, and ARMA Models Using ACF and PACF

This article explains how to recognize whether a stationary random time series follows a pure AR, pure MA, or mixed ARMA process by examining the patterns of the autocorrelation function (ACF) and the partial autocorrelation function (PACF).

AR modelARMA identificationMA model
0 likes · 7 min read
How to Identify AR, MA, and ARMA Models Using ACF and PACF
Model Perspective
Model Perspective
Aug 2, 2022 · Fundamentals

How ARMA Models Enable Accurate Time Series Forecasting

This article explains the recursive forecasting formulas for ARMA and MA(q) time‑series models, showing how forecasts depend only on past observations, how model invertibility ensures stability, and how estimated parameters are used in practical prediction.

ARMAMA(q)Statistical Modeling
0 likes · 2 min read
How ARMA Models Enable Accurate Time Series Forecasting
Model Perspective
Model Perspective
Aug 1, 2022 · Fundamentals

How to Build and Forecast ARMA Models: A Step-by-Step Guide

This article explains the process of constructing ARMA models, covering model identification, order selection using the AIC criterion, parameter estimation (including Python implementation), and diagnostic testing such as Ljung‑Box, before demonstrating how to generate forecasts from the fitted model.

AICARMAModel Selection
0 likes · 4 min read
How to Build and Forecast ARMA Models: A Step-by-Step Guide
Model Perspective
Model Perspective
Jul 31, 2022 · Fundamentals

Understanding ARMA: The Core of Stationary Time Series Models

This article explains the three main types of stationary time‑series models—AR, MA, and ARMA—detailing their definitions, back‑shift operator notation, polynomial representations, and the essential stationarity and invertibility conditions required for valid modeling.

ARMAModelingTime Series
0 likes · 3 min read
Understanding ARMA: The Core of Stationary Time Series Models
Model Perspective
Model Perspective
Jul 28, 2022 · Fundamentals

How to Forecast Seasonal Time Series with the Seasonal Coefficient Method

Learn a step-by-step approach to predict seasonal time series—such as product sales or climate data—using the seasonal coefficient method, illustrated with a quarterly refrigerator sales case study and a complete Python implementation that computes next year's quarterly forecasts.

PythonTime Seriesseasonal forecasting
0 likes · 4 min read
How to Forecast Seasonal Time Series with the Seasonal Coefficient Method
Model Perspective
Model Perspective
Jul 3, 2022 · Fundamentals

Explore 20+ Essential Modeling Articles: From Differential Equations to Machine Learning

This curated list groups recent articles on change and predictive models, covering topics such as war dynamics, population, epidemic spread, differential equations, regression, time‑series analysis, machine learning classifiers, and grey‑prediction techniques, providing students with ready references for diverse modeling approaches.

ModelingTime Seriesmachine learning
0 likes · 3 min read
Explore 20+ Essential Modeling Articles: From Differential Equations to Machine Learning
Alibaba Cloud Native
Alibaba Cloud Native
Jun 28, 2022 · Cloud Native

How Downsampling Supercharges Prometheus Queries for Large‑Scale Cloud‑Native Monitoring

This article explains why downsampling is essential for handling massive time‑series data in Prometheus, describes the aggregation rules and intervals, compares ARMS Prometheus' implementation with other solutions, and shows performance and accuracy results that demonstrate significant query speed improvements.

Cloud NativeDownsamplingPrometheus
0 likes · 15 min read
How Downsampling Supercharges Prometheus Queries for Large‑Scale Cloud‑Native Monitoring
Alibaba Cloud Developer
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
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
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
Open Source Linux
Open Source Linux
Apr 6, 2022 · Cloud Native

Why Prometheus’s TSDB Makes Monitoring Scalable: A Deep Dive

This article explains how Prometheus’s time‑series database handles massive monitoring data, from basic concepts and query examples to storage engine design, indexing strategies, and powerful data computation techniques such as recording rules.

PrometheusTSDBTime Series
0 likes · 8 min read
Why Prometheus’s TSDB Makes Monitoring Scalable: A Deep Dive
Python Programming Learning Circle
Python Programming Learning Circle
Apr 5, 2022 · Artificial Intelligence

Transforming Time Series Data into Supervised Learning Datasets with Pandas shift() and series_to_supervised()

This tutorial explains how to convert single‑variable and multi‑variable time‑series data into a supervised‑learning format using Pandas' shift() function and a custom series_to_supervised() helper, covering one‑step, multi‑step, and multivariate forecasting examples with complete Python code.

PythonTime Seriesforecasting
0 likes · 20 min read
Transforming Time Series Data into Supervised Learning Datasets with Pandas shift() and series_to_supervised()
DataFunSummit
DataFunSummit
Mar 1, 2022 · Artificial Intelligence

Alibaba's Smart Supply‑Chain Forecasting: Scenarios, Algorithm R&D, and Application Cases

This article details Alibaba's exploration of intelligent supply‑chain forecasting, covering scenario classification, three generations of prediction algorithms, the self‑developed Falcon model, performance evaluation, and real‑world cases such as Double 11 and live‑streaming, highlighting challenges and practical solutions.

Deep LearningTime Seriesai
0 likes · 18 min read
Alibaba's Smart Supply‑Chain Forecasting: Scenarios, Algorithm R&D, and Application Cases
Python Programming Learning Circle
Python Programming Learning Circle
Feb 28, 2022 · Artificial Intelligence

Time Series Data Preprocessing: Missing Value Imputation, Denoising, and Outlier Detection

This article explains essential time series preprocessing techniques—including data sorting, handling missing values with interpolation methods, applying rolling averages, Fourier transform denoising, and detecting anomalies using rolling statistics, isolation forests, and K‑means clustering—illustrated with Python code on the AirPassengers and Google stock datasets.

DenoisingPythonTime Series
0 likes · 9 min read
Time Series Data Preprocessing: Missing Value Imputation, Denoising, and Outlier Detection
Zhuanzhuan Tech
Zhuanzhuan Tech
Jan 5, 2022 · Operations

Design and Implementation of a Multi‑Dimensional Monitoring Platform Based on Prometheus and M3DB

This article details the background, research, architecture, performance testing, and deployment of a comprehensive monitoring system that leverages Prometheus, Grafana, and M3DB to provide flexible metric collection, automatic dashboard generation, and a custom alerting service for large‑scale business services.

AlertingMetricsTime Series
0 likes · 16 min read
Design and Implementation of a Multi‑Dimensional Monitoring Platform Based on Prometheus and M3DB
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 21, 2021 · Artificial Intelligence

Boost Time Series Forecasting with Autocorrelated Error Adjustment – A 5‑Line PyTorch Trick

This article explains a NeurIPS 2021 paper that introduces a learnable autocorrelation correction for neural network time‑series models, shows the underlying theory, provides concise PyTorch code implementing the adjustment, reports a ~17% average performance gain across datasets, and lists additional practical tricks for time‑series forecasting.

Neural NetworksPyTorchTime Series
0 likes · 6 min read
Boost Time Series Forecasting with Autocorrelated Error Adjustment – A 5‑Line PyTorch Trick
Code DAO
Code DAO
Dec 16, 2021 · Fundamentals

How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression

This article explains how mixing a Poisson process with a discrete k‑state hidden Markov model creates a Poisson HMM that captures autocorrelation in integer‑valued time‑series, detailing the model formulation, prediction via expectation over states, and parameter estimation using MLE or EM.

EMMLEMarkov model
0 likes · 11 min read
How Poisson Hidden Markov Models Enable Count‑Based Time‑Series Regression
DataFunSummit
DataFunSummit
Nov 16, 2021 · Artificial Intelligence

Industrial Intelligence: Current Status, Talent Requirements, Challenges, and AI Application Process

This article examines the state of industrial AI, discussing data and model challenges, the multidisciplinary talent needed, the DIKW framework, typical AI workflows, edge‑cloud architecture, real‑time processing tools, time‑series storage, service design patterns, and practical recommendations for deploying AI in manufacturing.

DIKWEdge ComputingIndustrial AI
0 likes · 24 min read
Industrial Intelligence: Current Status, Talent Requirements, Challenges, and AI Application Process
Architects Research Society
Architects Research Society
Nov 13, 2021 · Databases

Choosing the Right Databases for IoT Applications

The article explains why the Internet of Things generates massive, diverse data streams that require specialized databases, outlines key selection criteria, describes common IoT data types, and reviews several open‑source databases—InfluxDB, CrateDB, MongoDB, RethinkDB, SQLite, and Cassandra—highlighting their strengths for IoT workloads.

Data ManagementIoTNoSQL
0 likes · 10 min read
Choosing the Right Databases for IoT Applications
DataFunTalk
DataFunTalk
Nov 3, 2021 · Artificial Intelligence

Deep Learning for Time‑Series Modeling in Financial Risk Management

This article describes how a financial company leveraged deep‑learning sequence models to automatically extract features from massive time‑series data, improving risk‑assessment models and operational efficiency through a unified framework that includes data preprocessing, embedding, field and item aggregation, and end‑to‑end deployment.

Deep LearningModelingTime Series
0 likes · 10 min read
Deep Learning for Time‑Series Modeling in Financial Risk Management
Shopee Tech Team
Shopee Tech Team
Sep 23, 2021 · Big Data

Design and Architecture of the Boussole Real-Time Multi-Dimensional Data Analysis Engine

Boussole is Shopee’s real‑time analytics engine that transforms each dimension into key‑value pairs stored primarily in HBase, pre‑aggregates selected dimension combos, hashes metrics and tags, executes distributed PromQL queries with a CockroachDB‑inspired executor, applies Delta‑of‑Delta compression and point‑capping, and continues to evolve with adaptive pre‑aggregation and new storage models to maintain millisecond latency for massive multi‑dimensional analysis.

Distributed QueryPre-aggregationPromQL
0 likes · 24 min read
Design and Architecture of the Boussole Real-Time Multi-Dimensional Data Analysis Engine
MaGe Linux Operations
MaGe Linux Operations
Sep 18, 2021 · Operations

Why Prometheus’s TSDB Makes Massive Monitoring Data Manageable

The article explains how Prometheus, a data‑driven monitoring system, handles massive time‑series data using its TSDB storage engine, detailing concepts, query examples, storage characteristics, indexing mechanisms, and the benefits of pre‑computing rules for efficient monitoring at scale.

PrometheusTSDBTime Series
0 likes · 8 min read
Why Prometheus’s TSDB Makes Massive Monitoring Data Manageable
ITPUB
ITPUB
Sep 18, 2021 · Big Data

How to Use Time‑Series Data for Home Brewing and BBQ Temperature Monitoring

This article explains how hobbyists can leverage time‑series databases, Raspberry Pi, MQTT and Slack alerts to continuously monitor and control temperatures during beer fermentation and barbecue smoking, providing practical setup steps, alert thresholds, and visualization tips for reliable results.

IoTMQTTRaspberry Pi
0 likes · 9 min read
How to Use Time‑Series Data for Home Brewing and BBQ Temperature Monitoring
Didi Tech
Didi Tech
Jun 4, 2021 · Artificial Intelligence

Graph Convolutional Network for Shared Bike Demand Forecasting: Time Series Modeling and Multi‑Task Learning

The paper presents a graph convolutional network approach that leverages multi‑task learning and spectral graph convolutions to forecast shared‑bike inflow, outflow, and demand gaps across a city’s non‑Euclidean parking network, demonstrating improved accuracy over traditional time‑series baselines while noting scalability and directional graph limitations.

Demand ForecastingGCNGraph Neural Network
0 likes · 13 min read
Graph Convolutional Network for Shared Bike Demand Forecasting: Time Series Modeling and Multi‑Task Learning
Python Programming Learning Circle
Python Programming Learning Circle
May 24, 2021 · Artificial Intelligence

Useful Python Libraries for Data Science Beyond Pandas and NumPy

This article introduces a curated selection of lesser‑known Python libraries for data‑science tasks—including data acquisition, date‑time handling, imbalanced‑learning, fast keyword extraction, fuzzy string matching, time‑series modeling, 3‑D visualization, web‑app building, and reinforcement‑learning—providing installation commands and concise usage examples.

Data ScienceNLPTime Series
0 likes · 10 min read
Useful Python Libraries for Data Science Beyond Pandas and NumPy
Alibaba Cloud Developer
Alibaba Cloud Developer
May 13, 2021 · Databases

Why Lindorm TSDB Is Shaping the Future of Massive IoT Time‑Series Data

This article examines the explosive growth of IoT data, outlines the unique challenges of storing and querying massive time‑series datasets, reviews the evolution of time‑series databases, and explains how Alibaba Cloud's Lindorm TSDB leverages cloud‑native, multi‑model architecture to deliver high‑throughput, low‑cost, and scalable solutions for IoT, industrial and monitoring workloads.

Cloud NativeIoTLindorm
0 likes · 20 min read
Why Lindorm TSDB Is Shaping the Future of Massive IoT Time‑Series Data
MaGe Linux Operations
MaGe Linux Operations
Apr 18, 2021 · Operations

Why Prometheus’s TSDB Makes Massive Monitoring Feasible

This article explains how Prometheus turns complex monitoring into manageable tasks by introducing its core concepts, daily query examples, the advantages of its TSDB storage engine, and powerful data computation techniques that enable efficient large‑scale time‑series analysis.

PrometheusQueriesStorage Engine
0 likes · 7 min read
Why Prometheus’s TSDB Makes Massive Monitoring Feasible
21CTO
21CTO
Jan 20, 2021 · Databases

Why Time Series Databases Are the Future of Your Data

Time series databases let you retain full historical records, enabling analysis, visualization, machine learning and automation across domains like finance, weather and IoT, and the article explains why they’re essential, how they differ from traditional databases, and how to start using them.

Time Seriesdatabasesmachine learning
0 likes · 7 min read
Why Time Series Databases Are the Future of Your Data
JD Cloud Developers
JD Cloud Developers
Jan 5, 2021 · Databases

How ClickHouse Powers High‑Performance Time‑Series Data Management at JD’s JUST Engine

This article explains how JD’s JUST platform leverages the open‑source columnar database ClickHouse to store, query and analyze massive time‑series datasets, covering data modeling, lifecycle management, cluster architecture, write and query processes, scaling strategies and future enhancements.

Data ManagementDistributed SystemsScalability
0 likes · 21 min read
How ClickHouse Powers High‑Performance Time‑Series Data Management at JD’s JUST Engine
Ctrip Technology
Ctrip Technology
Dec 17, 2020 · Artificial Intelligence

Time Series Forecasting: Tools, Models, and Lessons from Ctrip

This article outlines Ctrip's approach to time series forecasting, covering background, common tools such as factor‑based models, traditional statistical methods like ARIMA, and machine‑learning techniques including tree and neural networks, and shares practical experiences on data splitting, feature engineering, model stability, and evaluation.

ARIMACtripTime Series
0 likes · 13 min read
Time Series Forecasting: Tools, Models, and Lessons from Ctrip
DataFunTalk
DataFunTalk
Dec 8, 2020 · Artificial Intelligence

Financial Big Data Risk Control Models: Techniques, Applications, and COVID‑19 Challenges

This article presents a comprehensive overview of financial big‑data risk control models at Du Xiaoman, covering traditional scoring cards, AI‑driven time‑series and text processing, graph‑based networks, model interpretability, probability calibration, stability analysis, and the specific challenges introduced by the COVID‑19 pandemic.

Artificial IntelligenceBig DataCredit Scoring
0 likes · 14 min read
Financial Big Data Risk Control Models: Techniques, Applications, and COVID‑19 Challenges
dbaplus Community
dbaplus Community
Dec 7, 2020 · Databases

Why InfluxDB’s max‑value‑per‑tag Error Occurs and How to Resolve It

This article explains the cause of InfluxDB’s max‑value‑per‑tag error when Prometheus remote‑writes high‑cardinality tags, analyzes why the built‑in memory index triggers OOM protection, and presents three practical solutions—including moving indexes to disk, storing high‑cardinality tags as fields, and filtering them before write—to ensure stable monitoring data persistence.

Database ConfigurationInfluxDBTime Series
0 likes · 11 min read
Why InfluxDB’s max‑value‑per‑tag Error Occurs and How to Resolve It
JD Tech Talk
JD Tech Talk
Nov 30, 2020 · Big Data

Scalable Time Series Similarity Search in Big Data: Partitioning, Dimensionality Reduction, and LSH Approaches

This article examines the challenges of performing time‑series similarity queries on massive datasets and presents three scalable solutions—partition‑based indexing, dimensionality‑reduction using MinHash, and a combined approach with Locality Sensitive Hashing—to reduce computation while preserving similarity accuracy.

Big DataLSHMinhash
0 likes · 10 min read
Scalable Time Series Similarity Search in Big Data: Partitioning, Dimensionality Reduction, and LSH Approaches
Architects Research Society
Architects Research Society
Nov 24, 2020 · Databases

Choosing the Right Databases for IoT Applications

The article explains why the massive, diverse data generated by the Internet of Things requires specialized databases, outlines key criteria for selecting a suitable database, and reviews several popular options such as InfluxDB, CrateDB, MongoDB, RethinkDB, SQLite, and Cassandra, highlighting their strengths for IoT workloads.

IoTNoSQLTime Series
0 likes · 11 min read
Choosing the Right Databases for IoT Applications
Ctrip Technology
Ctrip Technology
Sep 24, 2020 · Artificial Intelligence

Time Series Analysis and ARIMA Modeling Practice with Python

This article introduces time series fundamentals, classification, and challenges for internet businesses, then provides a step‑by‑step Python tutorial on ARIMA modeling—including data loading, stationarity testing, differencing, ACF/PACF analysis, AIC‑based order selection, model training, prediction, error evaluation, exogenous variable integration, and diagnostic checks.

ARIMAPythonStatistical Modeling
0 likes · 11 min read
Time Series Analysis and ARIMA Modeling Practice with Python
MaGe Linux Operations
MaGe Linux Operations
Sep 13, 2020 · Artificial Intelligence

Beyond Pandas: 10 Lesser‑Known Python Libraries Every Data Scientist Should Try

This article introduces a curated collection of lesser‑known Python libraries for data‑science tasks—including wget, pendulum, imbalanced‑learn, flashtext, fuzzywuzzy, pyflux, ipyvolume, dash, and gym—detailing their purpose, installation commands, and concise code examples to help practitioners expand their toolkit.

NLPPythonTime Series
0 likes · 9 min read
Beyond Pandas: 10 Lesser‑Known Python Libraries Every Data Scientist Should Try
Aikesheng Open Source Community
Aikesheng Open Source Community
Aug 24, 2020 · Operations

Prometheus Data Query Basics and Practical Usage Guide

This article introduces Prometheus' query language PromQL, explains instant and range vector selectors, label matching, offset handling, storage design, common functions and aggregation operators, and provides practical advice for efficient querying and avoiding performance issues.

OperationsPromQLPrometheus
0 likes · 13 min read
Prometheus Data Query Basics and Practical Usage Guide
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.

Parameter TuningProphetPython
0 likes · 14 min read
Prophet Parameter Tuning and Practical Guide for Time Series Forecasting
MaGe Linux Operations
MaGe Linux Operations
Jul 11, 2020 · Databases

Unlocking Time‑Series Power: A Deep Dive into TimescaleDB

This article introduces TimescaleDB—a PostgreSQL extension for time‑series data—explaining its core concepts, data‑model choices, architecture, native compression, and provides step‑by‑step installation instructions on a CentOS 7 environment.

Time SeriesTimescaleDBcompression
0 likes · 15 min read
Unlocking Time‑Series Power: A Deep Dive into TimescaleDB
DataFunTalk
DataFunTalk
Jun 13, 2020 · Artificial Intelligence

Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN

This article details how Amap leverages deep‑learning techniques—including temporal and auxiliary feature engineering, multi‑stage RNN models, Wide&Deep architectures, and an Attention‑TCN approach—to accurately identify and handle expired points of interest, improving map freshness and user experience.

Deep LearningPOI expirationRNN
0 likes · 13 min read
Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN
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.

LSTMProphetTime Series
0 likes · 16 min read
Intelligent Inventory Management: Comparing Prophet and LSTM for Time‑Series Forecasting
Tencent Advertising Technology
Tencent Advertising Technology
Jun 10, 2020 · Artificial Intelligence

Improving Advertising Inventory Forecasting with Deep Spatial‑Temporal Tensor Factorization

The article explains how advertising inventory forecasting—predicting how many users will see a specific ad—poses challenges due to fluctuating traffic and user segmentation, and describes a new deep spatial‑temporal tensor factorization model that dramatically improves prediction accuracy, scalability, and robustness for large‑scale ad platforms.

AdvertisingDeep LearningTime Series
0 likes · 11 min read
Improving Advertising Inventory Forecasting with Deep Spatial‑Temporal Tensor Factorization
Python Programming Learning Circle
Python Programming Learning Circle
May 21, 2020 · Artificial Intelligence

Time Series Forecasting and Anomaly Detection for API Traffic Using Seasonal Decomposition and ARIMA

The article presents a complete workflow for predicting next‑day API request volumes by exploring per‑minute traffic data, handling missing values, applying seasonal decomposition, training an ARIMA model on the trend component, and generating confidence intervals to flag anomalous spikes.

ARIMATime Seriesanomaly detection
0 likes · 12 min read
Time Series Forecasting and Anomaly Detection for API Traffic Using Seasonal Decomposition and ARIMA
Efficient Ops
Efficient Ops
May 11, 2020 · Operations

How Nightingale Transforms Monitoring for Scalable Stability

This article introduces Didi's open‑source monitoring system Nightingale, detailing its design, architecture, key improvements over Open‑Falcon, and how its flexible alerting and data handling capabilities support the full lifecycle of stability engineering in large‑scale operations.

AlertingDevOpsTime Series
0 likes · 23 min read
How Nightingale Transforms Monitoring for Scalable Stability
Amap Tech
Amap Tech
May 8, 2020 · Artificial Intelligence

Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models

The project develops a deep‑learning pipeline for Amap’s expired POI detection that integrates two‑year temporal trend features, industry and verification attributes, a variable‑length LSTM, a Wide‑Deep architecture, and a Wide‑Attention Temporal Convolutional Network, achieving higher accuracy and efficiency while outlining future macro‑and micro‑level enhancements.

Deep LearningPOI expirationRNN
0 likes · 15 min read
Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models
Efficient Ops
Efficient Ops
Feb 10, 2020 · Big Data

How Tencent Scales Elasticsearch for Massive Log, Search, and Time‑Series Workloads

Tencent leverages Elasticsearch at massive scale across log analytics, search services, and time‑series monitoring, addressing challenges of high availability, low cost, and high performance through kernel optimizations, resource‑aware throttling, cold‑data merging, rollup, caching, and open‑source contributions.

Cost OptimizationElasticsearchLog Analytics
0 likes · 20 min read
How Tencent Scales Elasticsearch for Massive Log, Search, and Time‑Series Workloads
Big Data Technology & Architecture
Big Data Technology & Architecture
Jan 19, 2020 · Big Data

Tencent's Elasticsearch Practices: Application Scenarios, Challenges, Optimizations, and Future Directions

This article details how Tencent leverages Elasticsearch for log analysis, search services, and time‑series data, outlines the specific challenges faced in high‑availability and cost‑efficiency, and presents the comprehensive optimization techniques and future open‑source contributions that improve performance, scalability, and reliability.

Big DataCost OptimizationElasticsearch
0 likes · 16 min read
Tencent's Elasticsearch Practices: Application Scenarios, Challenges, Optimizations, and Future Directions
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 16, 2020 · Artificial Intelligence

How Alibaba DAMO Academy Revolutionizes Anomaly Detection for Business and Machine Data

This article explains the evolution of Alibaba DAMO Academy’s time‑series anomaly detection technology, detailing its application to both machine and commercial data, the challenges of diverse data types, the new robust statistical models, automatic data classification, parameter recommendation, and real‑world case studies demonstrating improved accuracy and stability.

AlibabaData AnalyticsTime Series
0 likes · 14 min read
How Alibaba DAMO Academy Revolutionizes Anomaly Detection for Business and Machine Data
ITPUB
ITPUB
Jan 14, 2020 · Artificial Intelligence

Top 2019 AI Papers Loved by Reddit Users: Key Insights and Links

A curated collection of Reddit‑highlighted 2019 AI research papers, covering theoretical advances, computer‑vision breakthroughs, unsupervised learning methods, and time‑series forecasting, with summaries, key contributions, and direct links to each paper.

Computer VisionMeta LearningResearch Papers
0 likes · 6 min read
Top 2019 AI Papers Loved by Reddit Users: Key Insights and Links
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.

ARIMAHolt-WintersLSTM
0 likes · 12 min read
Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring
iQIYI Technical Product Team
iQIYI Technical Product Team
Dec 20, 2019 · Artificial Intelligence

Advertising Inventory Forecasting Using an LSTM-Based Deep Learning Model

The iQIYI advertising team introduced an LSTM‑based deep‑learning model that forecasts inventory by normalizing data, clustering dimensions, and embedding fine‑grained holiday features, achieving significantly lower bias than their Adaptive‑ARIMA baseline and improving generalization while reducing training resources.

Advertising ForecastingDeep LearningLSTM
0 likes · 10 min read
Advertising Inventory Forecasting Using an LSTM-Based Deep Learning Model
System Architect Go
System Architect Go
Oct 27, 2019 · Databases

Mastering InfluxDB: Data Types, Schema Design, and Index Configuration

This guide explains InfluxDB’s schemaless data types, best practices for designing tag‑and‑field schemas, and how to choose and configure its in‑memory or TSI indexes, including key parameters such as max‑series‑per‑database and max‑values‑per‑tag for optimal performance.

ConfigurationInfluxDBTime Series
0 likes · 6 min read
Mastering InfluxDB: Data Types, Schema Design, and Index Configuration
System Architect Go
System Architect Go
Oct 26, 2019 · Databases

Understanding InfluxDB Retention Policies and Shard Duration: A Deep Dive

This article explains InfluxDB's retention policy components—duration, replication, and shard duration—clarifies the concepts of shards and shard groups, describes default configurations, offers recommendations for shard group duration, and outlines practical considerations for performance and data management.

Database ArchitectureInfluxDBRetention Policy
0 likes · 5 min read
Understanding InfluxDB Retention Policies and Shard Duration: A Deep Dive
FunTester
FunTester
Oct 21, 2019 · Operations

Visualizing Long-Term API Latency with Java, Python, and Plotly

This guide shows how to extract average API response times from a MySQL database using Java, process the data with a Python script, and generate an interactive time-series chart with Plotly, providing a practical method for long-term performance monitoring.

API monitoringPythonTime Series
0 likes · 6 min read
Visualizing Long-Term API Latency with Java, Python, and Plotly
Tencent Database Technology
Tencent Database Technology
Sep 26, 2019 · Artificial Intelligence

Understanding X‑Pack Machine Learning in Elasticsearch: Features, Architecture, and Implementation

This article explains Elasticsearch X‑Pack's machine‑learning capabilities, covering supervised and unsupervised learning concepts, data preparation, task creation types, architecture components, data flow, result indices, and provides code examples for configuring and running ML jobs.

Data visualizationElasticsearchTime Series
0 likes · 16 min read
Understanding X‑Pack Machine Learning in Elasticsearch: Features, Architecture, and Implementation
Amap Tech
Amap Tech
Aug 6, 2019 · Artificial Intelligence

Boosting ETA Accuracy: How TCN Improves Historical Speed Prediction for Navigation

To enhance estimated arrival times in navigation, this article analyzes the shortcomings of traditional historical average methods and proposes a machine‑learning solution using Temporal Convolutional Networks combined with dynamic and static feature engineering, demonstrating reduced bad‑case rates and better handling of seasonal patterns.

ETA predictionTCNTime Series
0 likes · 11 min read
Boosting ETA Accuracy: How TCN Improves Historical Speed Prediction for Navigation
dbaplus Community
dbaplus Community
Jul 17, 2019 · Databases

Rethinking Prometheus TSDB: From V2 Bottlenecks to the Scalable V3 Design

This article examines the limitations of Prometheus's original V2 time‑series storage, proposes a block‑oriented V3 architecture that tackles series churn, write amplification, and indexing inefficiencies, and validates the new design with extensive benchmarks showing dramatic reductions in memory, CPU, and disk usage.

KubernetesPrometheusTSDB
0 likes · 36 min read
Rethinking Prometheus TSDB: From V2 Bottlenecks to the Scalable V3 Design
Alibaba Cloud Developer
Alibaba Cloud Developer
May 22, 2019 · Artificial Intelligence

Mastering Anomaly Detection: From Moving Averages to Isolation Forests

This comprehensive guide explores a wide range of anomaly detection techniques—including time‑series methods, statistical models, distance‑based approaches, tree‑based isolation forests, graph algorithms, behavior‑sequence Markov models, and supervised machine‑learning models—detailing their principles, formulas, and practical scenarios for detecting outliers in advertising, fraud, and system monitoring.

Isolation ForestTime Seriesanomaly detection
0 likes · 19 min read
Mastering Anomaly Detection: From Moving Averages to Isolation Forests
Ctrip Technology
Ctrip Technology
Apr 11, 2019 · Artificial Intelligence

An Overview of Anomaly Detection Methods and Their Applications

This article introduces the concept of anomaly detection, outlines common application scenarios such as ELT pipelines, feature engineering, A/B testing, and fraud detection, and reviews various detection methods—including statistical models, machine learning, rule‑based logic, and density‑based techniques—while discussing practical implementation considerations.

Data QualityTime Seriesanomaly detection
0 likes · 12 min read
An Overview of Anomaly Detection Methods and Their Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 19, 2019 · Artificial Intelligence

Unlocking Anomaly Detection: Techniques from Time Series to Deep Learning

This comprehensive guide explores anomaly (outlier) detection across diverse methods—including time‑series analysis, statistical tests, distance metrics, matrix factorization, graph approaches, behavior‑sequence modeling, and supervised machine‑learning models—highlighting their principles, formulas, and practical use cases such as fraud prevention and system monitoring.

Deep LearningTime Seriesanomaly detection
0 likes · 17 min read
Unlocking Anomaly Detection: Techniques from Time Series to Deep Learning
DataFunTalk
DataFunTalk
Mar 12, 2019 · Artificial Intelligence

Demand Forecasting Practices in Alibaba Retail: From Mean Models to Deep Learning

This article outlines Alibaba Retail's demand forecasting workflow, describing the evolution from simple mean and time‑series models to machine‑learning and deep‑learning approaches, the incorporation of feature engineering, operational plans, and methods for estimating prediction uncertainty to support intelligent replenishment.

Demand ForecastingRetailTime Series
0 likes · 13 min read
Demand Forecasting Practices in Alibaba Retail: From Mean Models to Deep Learning
JD Tech Talk
JD Tech Talk
Jan 16, 2019 · Artificial Intelligence

Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results

This article presents a detailed case study of building a purchase‑user prediction model by integrating Convolutional Neural Networks for feature extraction with Long Short‑Term Memory networks for time‑series forecasting, covering background, model structure, data augmentation, experimental results, and business impact.

CNNDeep LearningLSTM
0 likes · 10 min read
Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results
dbaplus Community
dbaplus Community
Jan 13, 2019 · Databases

January 2019 DB-Engines Newsletter: Latest Database Releases & Key Features

The January 2019 DB-Engines newsletter compiles the newest releases, feature highlights, and performance improvements across RDBMS, NoSQL, NewSQL, time‑series, big‑data, domestic, and cloud database families, while also explaining the ranking methodology and providing download links for the full issue.

Big DataNewSQLNoSQL
0 likes · 41 min read
January 2019 DB-Engines Newsletter: Latest Database Releases & Key Features
Ctrip Technology
Ctrip Technology
Sep 4, 2018 · Artificial Intelligence

Call Center Volume Forecasting and Staffing Optimization at Ctrip: From Data Cleaning to V2.0 Predictive System

This article describes Ctrip's call‑center staffing challenge, detailing data cleaning, trend analysis, feature engineering, the initial ARIMAX‑Fourier model (V1.0), its limitations, and the improved V2.0 solution that combines TBATS, ARIMA residuals and XGBoost, achieving up to 89.5% prediction accuracy.

Time SeriesXGBoostcall center
0 likes · 9 min read
Call Center Volume Forecasting and Staffing Optimization at Ctrip: From Data Cleaning to V2.0 Predictive System
360 Tech Engineering
360 Tech Engineering
Aug 24, 2018 · Artificial Intelligence

Time Series Forecasting with Seasonal Decomposition and ARIMA

This article explains how to process a periodic time‑series, split it into training and test sets, smooth the data, decompose it with statsmodels' seasonal_decompose, forecast the trend using an ARIMA model, and evaluate the results with RMSE, providing a practical workflow for accurate forecasting.

ARIMAPythonStatsmodels
0 likes · 5 min read
Time Series Forecasting with Seasonal Decomposition and ARIMA
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
Efficient Ops
Efficient Ops
Apr 26, 2018 · Operations

How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms

This article explains how 360’s network operations team uses time‑series analysis, statistical thresholds, EWMA, dynamic limits, and machine‑learning models such as K‑Means and Isolation Forest to automatically detect, locate, and remediate traffic anomalies across massive data‑center exits.

AI OpsNetwork MonitoringTime Series
0 likes · 15 min read
How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms
Efficient Ops
Efficient Ops
Feb 6, 2018 · Operations

Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves

The article recounts the author’s 2017‑onward journey building an intelligent operations platform at Tencent, detailing challenges such as legacy thresholds, AIOps talent shortage, and lack of frameworks, and explains how a two‑stage hybrid unsupervised‑supervised model was devised to automatically detect anomalies across millions of KPI time‑series, enabling scalable root‑cause analysis and cost optimization.

OperationsTime Seriesaiops
0 likes · 7 min read
Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves
ITPUB
ITPUB
Jan 16, 2018 · Databases

10 Groundbreaking Database Systems Launched in 2017

A 2017 roundup highlights ten innovative database releases—including a time‑series extension for PostgreSQL, a multi‑model Azure service, Google’s globally distributed Spanner, Amazon’s Neptune graph service, and several open‑source cloud‑native databases—detailing their key features, architectures, and intended use cases.

DistributedTime Seriescloud
0 likes · 10 min read
10 Groundbreaking Database Systems Launched in 2017
Java High-Performance Architecture
Java High-Performance Architecture
Oct 13, 2017 · Databases

Why Redis Added Streams and How to Use Them Effectively

Redis introduced the Streams data type to address limitations of sorted sets, lists, and Pub/Sub for handling continuous data flows, offering features like field-value entries, efficient range queries, and client blocking with ID tracking, and the article explains its design, commands, and usage examples.

Data StructuresStreamsTime Series
0 likes · 7 min read
Why Redis Added Streams and How to Use Them Effectively
Alibaba Cloud Developer
Alibaba Cloud Developer
May 17, 2017 · Databases

How Alibaba Tackles the Massive Challenges of Time‑Series Data Storage

This article details Alibaba's middleware team's exploration of time‑series data characteristics, real‑world monitoring scenarios, the limitations of traditional databases, and the evolution of their custom HiTSDB solution that combines inverted indexing, high‑compression algorithms, and distributed aggregation to meet massive write and query demands.

AlibabaBig DataHiTSDB
0 likes · 25 min read
How Alibaba Tackles the Massive Challenges of Time‑Series Data Storage
Ctrip Technology
Ctrip Technology
Mar 3, 2017 · Databases

Design and Implementation of CFL: A MySQL Time‑Series Storage Engine

This article presents the design, architecture, and performance evaluation of CFL, a MySQL time‑series storage engine developed by Ctrip, detailing its functional and interface layers, storage strategy, and benchmark results compared with InnoDB, MyISAM, and other engines.

CFLStorage EngineTime Series
0 likes · 9 min read
Design and Implementation of CFL: A MySQL Time‑Series Storage Engine
ITPUB
ITPUB
Jun 24, 2016 · Databases

How Ctrip Built a Fast MySQL-Based Time‑Series Storage Engine (CFL)

This article details Ctrip's motivation, design, implementation, performance evaluation, and future plans for a custom MySQL storage engine called CFL that efficiently stores time‑series data by leveraging MySQL's replication and a sequential write‑optimized file format.

CFLStorage EngineTime Series
0 likes · 15 min read
How Ctrip Built a Fast MySQL-Based Time‑Series Storage Engine (CFL)