forecasting | BestHub

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.

AutomationRisk Mitigationforecasting

0 likes · 9 min read

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

Model Perspective

Feb 12, 2025 · Fundamentals

Can You Really Predict the Future? Lessons from Data, Causality, and Forecasting

Using a year‑long revenue dataset from an online‑education firm, this article examines how description, causal explanation, and statistical modeling together reveal patterns, uncover underlying drivers, and highlight the limits and uncertainties of forecasting future performance.

business analyticscausal inferencedata analysis

0 likes · 7 min read

Can You Really Predict the Future? Lessons from Data, Causality, and Forecasting

DataFunSummit

Nov 24, 2024 · Artificial Intelligence

AI-Driven Forecasting in Modern Supply Chains: Methods, Models, and Practical Guidance

The article explains how modern supply chain forecasting has shifted from qualitative expert judgment to quantitative AI-driven methods such as DeepAR, ensemble learning, and Transformers, and outlines the skills needed for practitioners to build effective predictive models.

AIDeepARTransformer

0 likes · 10 min read

AI-Driven Forecasting in Modern Supply Chains: Methods, Models, and Practical Guidance

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.

Feature EngineeringMachine LearningPython

0 likes · 14 min read

Time Series Feature Engineering Techniques in Python

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.

AntFluxArtificial IntelligenceMachine Learning

0 likes · 17 min read

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

DataFunTalk

Jul 14, 2024 · Artificial Intelligence

Time Series and Machine Learning – An Overview and Book Introduction

The article introduces the rapid rise of large language models, the abundance of time‑series data in many sectors, and explains how combining machine‑learning and deep‑learning techniques with time‑series analysis has become a research hotspot, culminating in a new book that systematically covers theory, methods, and real‑world applications.

AIAnomaly DetectionMachine Learning

0 likes · 10 min read

Time Series and Machine Learning – An Overview and Book Introduction

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.

Decision SupportGrey TheoryLimited Data

0 likes · 8 min read

Unlocking Grey Theory: Predicting with Incomplete Data

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.

AntFluxTime Series AIforecasting

0 likes · 19 min read

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.

Machine Learningexplainable AIforecasting

0 likes · 14 min read

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

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.

AIAntFluxforecasting

0 likes · 17 min read

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

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.

DidiOffline Reinforcement LearningRide-hailing

0 likes · 16 min read

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

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.

Machine LearningPCAcorrelation

0 likes · 12 min read

Statistical and Machine Learning Metrics for Data Analysis

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.comMachine Learningforecasting

0 likes · 17 min read

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

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

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

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

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.

ARMAPythonforecasting

0 likes · 18 min read

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

Model Perspective

Dec 19, 2022 · Fundamentals

Master Moving Average & Exponential Smoothing for Time Series Forecasting

This article explains the principles, formulas, and Python implementations of simple and double moving averages, as well as single, double, and Holt's exponential smoothing methods, illustrating each technique with real data cases and code samples.

Pythonexponential smoothingforecasting

0 likes · 16 min read

Master Moving Average & Exponential Smoothing for Time Series Forecasting

Python Programming Learning Circle

Nov 21, 2022 · Artificial Intelligence

Transforming Time Series Data into Supervised Learning Datasets with Pandas

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

Machine LearningPythonforecasting

0 likes · 18 min read

Transforming Time Series Data into Supervised Learning Datasets with Pandas

Model Perspective

Nov 5, 2022 · Fundamentals

Mastering ARMA: Build and Forecast Time Series Models with AIC and Python

This article explains how to identify, order‑select, estimate parameters, validate, and forecast ARMA time‑series models, covering the Akaike Information Criterion, various estimation techniques, and diagnostic tests such as the Ljung‑Box test, with practical Python implementation guidance.

AICARMAPython

0 likes · 4 min read

Mastering ARMA: Build and Forecast Time Series Models with AIC and Python