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DeepHub IMBA
DeepHub IMBA
Apr 22, 2026 · Artificial Intelligence

A Survey of Time Series Forecasting Augmentation: Frequency Domain, Decomposition, and Patch Methods

The article reviews why classic classification augmentations fail for forecasting, outlines a taxonomy of effective time‑series augmentation techniques—including frequency‑domain, decomposition, and patch‑based methods—details the Temporal Patch Shuffle (TPS) pipeline, and presents extensive experiments showing TPS achieves state‑of‑the‑art improvements across long‑term, short‑term, and classification tasks.

Time Seriesdata augmentationforecasting
0 likes · 17 min read
A Survey of Time Series Forecasting Augmentation: Frequency Domain, Decomposition, and Patch Methods
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 3, 2026 · Artificial Intelligence

How HORAI Uses Large‑Scale Multimodal Pretraining to Boost Time‑Series Forecasting and Anomaly Detection

The article reviews the HORAI model, which introduces a frequency‑enhanced multimodal pretraining paradigm and the massive MM‑TS dataset, showing that integrating derived images, endogenous text, and real‑world news dramatically improves zero‑shot forecasting and anomaly detection across six domains.

HORAIMultimodal LearningTime Series
0 likes · 23 min read
How HORAI Uses Large‑Scale Multimodal Pretraining to Boost Time‑Series Forecasting and Anomaly Detection
Model Perspective
Model Perspective
Jan 30, 2026 · Fundamentals

Mastering Multi‑Dimensional Forecasting: From Peer Benchmarks to System‑Level Insights

This article presents a comprehensive framework for forecasting that combines peer (same‑level) comparison, bottom‑up decomposition, top‑down system thinking, time‑series analysis, causal modeling, and scenario simulation, while highlighting each method's strengths, limitations, and practical wisdom for effective decision‑making.

PredictionTime Seriescausality
0 likes · 12 min read
Mastering Multi‑Dimensional Forecasting: From Peer Benchmarks to System‑Level Insights
Model Perspective
Model Perspective
Nov 23, 2025 · Fundamentals

Mathematical Models Driving Solar, Wind, and Storage Systems

This article reviews key mathematical modeling techniques for renewable energy, covering solar photovoltaic irradiance and efficiency models, wind turbine power and Weibull wind speed distributions, battery storage dynamics and capacity optimization, microgrid energy management, and forecasting methods such as ARMA and MPC, highlighting their role in efficient clean energy deployment.

energy storageforecastingmicrogrid
0 likes · 10 min read
Mathematical Models Driving Solar, Wind, and Storage Systems
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 8, 2025 · Artificial Intelligence

Time-Series Paper Digest: Nov 1‑7 2025 Highlights

This digest summarizes three recent AI papers—DoFlow, Forecast2Anomaly, and ForecastGAN—detailing their causal generative flow model for interventions, a retrieval‑augmented framework for zero‑shot anomaly prediction, and a decomposition‑based adversarial approach that improves multi‑horizon forecasting across diverse datasets.

Deep LearningTime Seriesanomaly detection
0 likes · 8 min read
Time-Series Paper Digest: Nov 1‑7 2025 Highlights
Model Perspective
Model Perspective
Sep 26, 2025 · Fundamentals

Unlocking Insights with Grey Relational Analysis and Grey Prediction Models

This article introduces the core principles of Grey Relational Analysis and the Grey Prediction Model, explains their calculation steps, and demonstrates how they can be applied across engineering, economics, and environmental fields to analyze limited data, select key indicators, evaluate systems, and forecast trends.

Grey Prediction ModelGrey Theorydata modeling
0 likes · 8 min read
Unlocking Insights with Grey Relational Analysis and Grey Prediction Models
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 20, 2025 · Artificial Intelligence

Recent Time-Series Paper Summaries (Sep 13‑19, 2025)

This article summarizes four recent time‑series forecasting papers, covering a universal delay‑embedding foundation model, a dual causal network that leverages exogenous variables, a distribution‑aware alignment plug‑in called TimeAlign, and a shapelet‑based framework for interpretable directional forecasting in noisy financial markets.

Time Seriescausal networkfinancial markets
0 likes · 9 min read
Recent Time-Series Paper Summaries (Sep 13‑19, 2025)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 16, 2025 · Artificial Intelligence

Paper Review: HGTS‑Former – A Hierarchical Hypergraph Transformer for Multivariate Time‑Series Analysis

The HGTS‑Former model introduces a hierarchical hypergraph backbone combined with a Transformer to capture high‑order and dynamic dependencies in multivariate time‑series data, and experimental results on eight datasets show it consistently outperforms state‑of‑the‑art methods in both long‑term forecasting and interpolation tasks.

HGTS-FormerHypergraphTransformer
0 likes · 11 min read
Paper Review: HGTS‑Former – A Hierarchical Hypergraph Transformer for Multivariate Time‑Series Analysis
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 6, 2025 · Artificial Intelligence

Time Series Paper Digest (Aug 23–Sep 5 2025)

It presents concise summaries of six recent arXiv papers on unsupervised domain adaptation, efficient forecasting, SHAP explanations, text‑reinforced multimodal forecasting, online prediction with feature adjustment, zero‑shot forecasting zoo, and a new anomaly‑detection metric, highlighting methods, datasets, and results.

Multimodal LearningOnline LearningSHAP
0 likes · 16 min read
Time Series Paper Digest (Aug 23–Sep 5 2025)
Data Party THU
Data Party THU
Aug 2, 2025 · Artificial Intelligence

Timer 3.0: Generative Time‑Series Large Model Breaks Prediction Limits

The article summarizes Professor Long Mingsheng’s presentation on the Timer series of time‑series large models, detailing the three core challenges of industrial time‑series analysis, the evolution from statistical methods to generative models, and the technical breakthroughs of Timer 1.0, 2.0 and 3.0 that enable multi‑task, long‑context, and trillion‑scale forecasting for industrial digital transformation.

Industrial AIIoTDBLarge Model
0 likes · 14 min read
Timer 3.0: Generative Time‑Series Large Model Breaks Prediction Limits
Model Perspective
Model Perspective
Jul 8, 2025 · Big Data

Why Historical Data Can Mislead Your Forecasts—and What to Do Instead

The article explains how relying solely on historical data for prediction often leads to large errors because future structural changes and missing variables are ignored, and it proposes causal modeling, scenario simulation, and real‑time signals as more reliable forecasting approaches.

Big Datacausal modelingforecasting
0 likes · 9 min read
Why Historical Data Can Mislead Your Forecasts—and What to Do Instead
37 Interactive Technology Team
37 Interactive Technology Team
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
Dual-Track Product Journal
Dual-Track Product Journal
Apr 11, 2025 · Operations

Why Your Replenishment System Traps You in a ‘More Restock, More Shortage’ Loop—and How to Fix It

This article dissects common failures in e‑commerce replenishment—such as hot‑product black holes, slow‑moving stock graves, and supply‑chain avalanches—and presents a seven‑step framework of dynamic forecasting, tiered strategies, distributed inventory, and automated safeguards to stabilize inventory levels.

AutomationOperationsSupply Chain
0 likes · 9 min read
Why Your Replenishment System Traps You in a ‘More Restock, More Shortage’ Loop—and How to Fix It
Python Programming Learning Circle
Python Programming Learning Circle
Sep 10, 2024 · Artificial Intelligence

Time Series Feature Engineering Techniques in Python

This article explains how to extract a variety of date‑time based features—including date, time, lag, rolling, expanding, and domain‑specific attributes—from a time‑series dataset using pandas, and discusses proper validation strategies for building reliable forecasting models.

Time Seriesfeature engineeringforecasting
0 likes · 14 min read
Time Series Feature Engineering Techniques in Python
DataFunTalk
DataFunTalk
Aug 1, 2024 · Artificial Intelligence

Ant Group's Time Series AI Practices: AntFlux Engine and Real‑World Applications

This article presents Ant Group's comprehensive time‑series AI solutions, detailing the AntFlux platform, the evolution from statistical to deep and large‑scale models—including Time‑LLM, iTransformer, and SLOTH—and illustrating how these technologies empower business insight, forecasting, decision‑making, and green computing across diverse scenarios.

AntFluxTime Seriesforecasting
0 likes · 17 min read
Ant Group's Time Series AI Practices: AntFlux Engine and Real‑World Applications
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
May 7, 2024 · Artificial Intelligence

How Alibaba’s New Time‑Series Models Are Redefining Forecasting and Anomaly Detection

Alibaba Cloud’s big‑data research team announced four groundbreaking time‑series papers accepted at ICLR 2024, ICDE 2024 and WWW 2024, introducing models such as Pathformer, ContraLSP, MACE, and LARA that advance multi‑scale forecasting, explainable AI, and efficient anomaly detection for intelligent operations.

AITime Seriesanomaly detection
0 likes · 11 min read
How Alibaba’s New Time‑Series Models Are Redefining Forecasting and Anomaly Detection
Model Perspective
Model Perspective
Apr 21, 2024 · Fundamentals

Unlocking Grey Theory: Predicting with Incomplete Data

Grey Theory, introduced by Deng Julong in 1982, offers a mathematical framework for analyzing systems with incomplete or uncertain data, using techniques like generated series and the GM(1,1) model to enable reliable forecasting and decision‑making across fields such as economics, environment, and product lifecycle analysis.

Grey TheoryLimited Datadata modeling
0 likes · 8 min read
Unlocking Grey Theory: Predicting with Incomplete Data
DataFunTalk
DataFunTalk
Apr 11, 2024 · Artificial Intelligence

Ant Group’s Time Series AI Practices: AntFlux Engine and Real‑World Applications

Ant Group shares its time‑series AI practice, detailing the AntFlux intelligent engine, the evolution of statistical and deep learning models, large‑scale time‑series platforms, and real‑world applications across finance, cloud, and green computing, illustrating challenges, innovations, and future directions.

AntFluxIndustrial AITime Series AI
0 likes · 19 min read
Ant Group’s Time Series AI Practices: AntFlux Engine and Real‑World Applications
JD Retail Technology
JD Retail Technology
Feb 26, 2024 · Artificial Intelligence

Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain

The article details JD’s smart supply‑chain innovations, describing an explainable AI forecasting method that boosts prediction accuracy while maintaining interpretability, and an end‑to‑end inventory management model based on multi‑quantile RNNs that improves replenishment decisions, reduces costs, and enhances overall operational efficiency.

Supply Chainexplainable AIforecasting
0 likes · 14 min read
Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain
DataFunSummit
DataFunSummit
Feb 12, 2024 · Artificial Intelligence

Ant Group's Time Series AI Practices and the AntFlux Intelligent Engine

This article presents Ant Group's comprehensive time‑series AI solutions, covering the business value of temporal data, the evolution of statistical and deep learning models, large‑scale time‑series platforms such as AntFlux, and real‑world applications ranging from financial forecasting to green computing.

AIAntFluxTime Series
0 likes · 17 min read
Ant Group's Time Series AI Practices and the AntFlux Intelligent Engine
Didi Tech
Didi Tech
Jun 13, 2023 · Operations

Supply-Demand Dynamics and Regulation Techniques in Didi’s Ride-Hailing Platform

Didi balances ride‑hailing supply and demand by forecasting regional needs with time‑series and deep‑learning models, then optimally repositioning drivers through integer programming and refining policies via imitation and offline reinforcement learning, ultimately enhancing passenger experience and platform efficiency.

DidiRide Hailingforecasting
0 likes · 16 min read
Supply-Demand Dynamics and Regulation Techniques in Didi’s Ride-Hailing Platform
DaTaobao Tech
DaTaobao Tech
May 22, 2023 · Artificial Intelligence

Statistical and Machine Learning Metrics for Data Analysis

The article presents a practical toolbox of statistical and machine‑learning metrics—including short‑term growth rates, CAGR, Excel forecasting functions, Wilson score adjustment, sigmoid decay weighting, correlation coefficients, KL divergence, elbow detection with KneeLocator, entropy‑based weighting, PCA, and TF‑IDF—offering concise formulas and code snippets for data analysis without deep theory.

PCAcorrelationdata analysis
0 likes · 12 min read
Statistical and Machine Learning Metrics for Data Analysis
DataFunSummit
DataFunSummit
Feb 2, 2023 · Artificial Intelligence

Exploring Super Automation in JD Supply Chain: Architecture, Applications, and Future Outlook

This article presents JD's super automation approach for its supply chain, detailing the business background, challenges, AI‑driven forecasting, procurement, intelligent allocation, inventory clearing, integrated decision making, and future directions toward fully automated, optimal end‑to‑end operations.

JD.comSupply Chainforecasting
0 likes · 17 min read
Exploring Super Automation in JD Supply Chain: Architecture, Applications, and Future Outlook
DataFunTalk
DataFunTalk
Jan 22, 2023 · Artificial Intelligence

Alibaba Digital Supply Chain: From Digitalization to Intelligent Forecasting

This presentation outlines Alibaba's digital supply chain strategy, detailing the data, analysis, and decision challenges, the multi‑layer digitalization and intelligent solutions, the evolution of the Falcon forecasting technology, and the Alibaba DChain Forecast SaaS product, with case studies and a Q&A.

AIAlibabaDigitalization
0 likes · 22 min read
Alibaba Digital Supply Chain: From Digitalization to Intelligent Forecasting
Model Perspective
Model Perspective
Jan 5, 2023 · Fundamentals

Modeling Age‑Structured Populations with the Leslie Matrix in Python

This article explains the Leslie matrix model for age‑structured population forecasting, outlines its mathematical formulation, demonstrates how to build and solve it using Python code, and shows how to derive demographic indicators such as average age, lifespan, aging index, and dependency ratio.

Leslie matrixPythonage‑structured model
0 likes · 9 min read
Modeling Age‑Structured Populations with the Leslie Matrix in Python
Model Perspective
Model Perspective
Dec 26, 2022 · Fundamentals

Mastering Holt-Winters: Additive Model Explained with Python Code

This article introduces the Holt‑Winters additive exponential smoothing model, explains its mathematical formulation and when to use additive versus multiplicative versions, and provides Python examples using statsmodels to fit both exponential and linear trend variations, illustrated with plots.

Holt-WintersPythonexponential smoothing
0 likes · 5 min read
Mastering Holt-Winters: Additive Model Explained with Python Code
Model Perspective
Model Perspective
Dec 20, 2022 · Fundamentals

Master Stationary Time Series & ARMA Models: Theory, Examples, Python Code

This article explains the fundamentals of weakly stationary time series, defines mean, variance, autocovariance, and autocorrelation functions, introduces AR, MA, ARMA, and ARIMA models, discusses model identification using ACF/PACF, selection criteria like AIC/SBC, diagnostic testing, and provides Python statsmodels code examples for implementation.

ARMAPythonStatsmodels
0 likes · 18 min read
Master Stationary Time Series & ARMA Models: Theory, Examples, Python Code
Model Perspective
Model Perspective
Sep 25, 2022 · Artificial Intelligence

Mastering ARCH and GARCH Models in Python: From Theory to Forecasting

This guide explains the limitations of ARIMA‑type models for handling changing variance, introduces ARCH and GARCH as solutions, and walks through Python implementations—including data generation, model fitting, and forecasting—complete with code snippets and visualizations.

ArchGARCHPython
0 likes · 7 min read
Mastering ARCH and GARCH Models in Python: From Theory to Forecasting
Model Perspective
Model Perspective
Sep 9, 2022 · Fundamentals

What Is a Time Series and How Do We Analyze Its Patterns?

A time series is a chronologically ordered set of interrelated data points whose analysis involves studying its development patterns and forecasting future behavior, with classifications based on dimensionality, continuity, statistical properties such as stationarity, and distribution types like Gaussian or non‑Gaussian.

Time Seriesforecastingmultivariate
0 likes · 2 min read
What Is a Time Series and How Do We Analyze Its Patterns?
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 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
Jun 21, 2022 · Fundamentals

Unlocking Grey System Theory: Modeling Uncertain Systems with Minimal Data

This article introduces Grey System Theory, explains its origins, core concepts of partial information, advantages over black‑box models, data accumulation/reduction techniques, and demonstrates a Python case study that improves forecasting accuracy for short‑term exponential trends.

Modelingdata-accumulationforecasting
0 likes · 8 min read
Unlocking Grey System Theory: Modeling Uncertain Systems with Minimal Data
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.

AIDeep LearningTime Series
0 likes · 18 min read
Alibaba's Smart Supply‑Chain Forecasting: Scenarios, Algorithm R&D, and Application Cases
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
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
JD Retail Technology
JD Retail Technology
Nov 18, 2020 · Industry Insights

How JD.com Used AI and Operations Science to Power 11.11 Supply‑Chain Success

JD.com's intelligent supply‑chain team combined AI‑driven forecasting, S&OP planning, real‑time inventory response, smart fulfillment, anti‑arbitrage detection, price governance, and precise C2M delivery to dramatically cut costs, improve inventory turnover, and deliver a seamless 11.11 shopping experience.

LogisticsOperationsPrice Optimization
0 likes · 18 min read
How JD.com Used AI and Operations Science to Power 11.11 Supply‑Chain Success
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
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
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.

AIAdvertisingDeep Learning
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
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
JD Tech
JD Tech
Dec 6, 2018 · Operations

Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain

JD's chief scientist Shen Zuo‑jun explains how shortening the decision chain with end‑to‑end algorithms and intelligent multi‑level replenishment dramatically improves inventory turnover, stock availability, and forecasting accuracy, showcasing a novel supply‑chain research direction that integrates AI, big data, and human expertise.

End-to-EndOperationsforecasting
0 likes · 9 min read
Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain
JD Tech
JD Tech
Sep 29, 2018 · Artificial Intelligence

JD.com Prediction Technology: Architecture, Applications, and Future Directions

The article outlines JD.com's evolution of prediction technology from early book‑category sales forecasting to a comprehensive AI‑driven platform that supports sales, order, and GMV forecasts, describes its modular architecture and core algorithm choices, and discusses future enhancements for smarter supply‑chain collaboration.

Big DataPredictionforecasting
0 likes · 6 min read
JD.com Prediction Technology: Architecture, Applications, and Future Directions
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
Liulishuo Tech Team
Liulishuo Tech Team
Dec 2, 2016 · Product Management

Estimating Daily Active Users (DAU) Using New Users and Retention Modeling

This article explains how to estimate future daily active users (DAU) for an app by modeling the accumulation of new users and their retention decay, addressing challenges of changing historical retention rates and proposing a combined approach using recent averages and curve‑fitted functions to predict long‑term user activity.

DAUforecastingproduct analytics
0 likes · 9 min read
Estimating Daily Active Users (DAU) Using New Users and Retention Modeling