Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)

This article summarizes eight newly released AI papers on multivariate time‑series forecasting and anomaly detection, detailing each work's motivation, proposed methodology, key innovations such as CRIB, TS‑JEPA, DSAT‑HD, DIMIGNN, ASTGI, IndexNet, TsLLM, Moon, TimeSeriesScientist, MLG‑4TS, and Augur, and reports their experimental validation on real‑world datasets.

Deep LearningTransformeranomaly detection

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Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)

Ctrip Technology

Sep 29, 2024 · Artificial Intelligence

Structured Components-based Neural Network (SCNN) for Multivariate Time Series Forecasting: Theory, Implementation, and Business Application

This article presents the SCNN model for multivariate time series forecasting, explains its decomposition into long‑term, seasonal, short‑term, and co‑evolving components, details the neural‑network‑based fusion and loss design, provides Python code snippets, and demonstrates its practical deployment for business volume prediction at Ctrip.

Neural NetworkPredictionPython

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Structured Components-based Neural Network (SCNN) for Multivariate Time Series Forecasting: Theory, Implementation, and Business Application

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

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

Model Perspective

Oct 10, 2022 · Artificial Intelligence

Predict Air Pollution with Multivariate LSTM in Keras: A Step‑by‑Step Guide

This tutorial explains how to build, train, and evaluate a multivariate LSTM model using Keras for hourly air‑pollution forecasting, covering data preparation, model design, prediction, and inverse scaling back to original units.

KerasLSTMPython

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Predict Air Pollution with Multivariate LSTM in Keras: A Step‑by‑Step Guide

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

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

Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)

Structured Components-based Neural Network (SCNN) for Multivariate Time Series Forecasting: Theory, Implementation, and Business Application

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

Predict Air Pollution with Multivariate LSTM in Keras: A Step‑by‑Step Guide

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

Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)