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Time Series Forecasting

68 articles · Page 1 of 1
Machine Heart
Machine Heart
Sep 25, 2026 · Artificial Intelligence

TimePrism: Probabilistic Parallel Generation Predates Jev by a Year

CUHK's TimePrism paper, accepted at ICLR 2026, introduced a probabilistic scenarios paradigm that outputs multiple futures with explicit probabilities in a single forward pass using only three linear layers, outperforming complex models on benchmarks—a year before Jev popularized similar decision-oriented, parallel probability generation for AI agents.

CUHKDecision-Oriented AIExplicit Probabilities
0 likes · 11 min read
TimePrism: Probabilistic Parallel Generation Predates Jev by a Year
IT Services Circle
IT Services Circle
Sep 12, 2026 · Industry Insights

This Week's Top 14 GitHub Open Source Projects: AI Agents, DevTools & Voice Studios

A curated roundup of 14 trending GitHub repositories covering scientific research agents, virtual iPhone testing, SEO automation, AI refusal modification, patent workflows, voice cloning, coding agents, time-series forecasting, LLM training internals, MCP servers, screenshot-to-code, App Store tooling, voice input, and architecture diagram generation.

AI AgentsArchitecture DiagramsGitHub
0 likes · 14 min read
This Week's Top 14 GitHub Open Source Projects: AI Agents, DevTools & Voice Studios
DeepHub IMBA
DeepHub IMBA
Sep 4, 2026 · Artificial Intelligence

TimesFM-3: Google's Native Multivariate Forecasting with One-Pass Decoding

Google's TimesFM-3 introduces native multivariate time series forecasting with non-autoregressive decoding, enabling joint predictions across multiple targets and covariates in a single forward pass, outperforming competitors on benchmarks while offering zero-shot generalization and probabilistic outputs.

Foundation ModelsProbabilistic ForecastingTime Series Forecasting
0 likes · 15 min read
TimesFM-3: Google's Native Multivariate Forecasting with One-Pass Decoding
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jun 13, 2026 · Artificial Intelligence

Paper Reading: TimeGMM – An Adaptive GMM Framework for Probabilistic Time‑Series Forecasting

TimeGMM introduces an adaptive Gaussian‑mixture‑model framework with reversible instance normalization, a dual‑branch time encoder, and a conditional decoder, achieving up to 22.48 % improvement in CRPS and 21.23 % in NMAE over state‑of‑the‑art probabilistic forecasting methods across multiple benchmark datasets.

Adaptive NormalizationGaussian Mixture ModelProbabilistic Forecasting
0 likes · 15 min read
Paper Reading: TimeGMM – An Adaptive GMM Framework for Probabilistic Time‑Series Forecasting
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jun 6, 2026 · Artificial Intelligence

ProbFM: Deep Evidential Regression for Uncertainty Decomposition in Financial Forecasting

ProbFM introduces a Transformer‑based framework that leverages deep evidential regression to separate epistemic and aleatoric uncertainty in time‑series forecasting, and demonstrates on cryptocurrency returns that this decomposition retains competitive prediction accuracy while enabling risk‑aware trading strategies with superior risk‑adjusted returns.

Deep Evidential RegressionFinancial PredictionProbFM
0 likes · 13 min read
ProbFM: Deep Evidential Regression for Uncertainty Decomposition in Financial Forecasting
Data Party THU
Data Party THU
May 26, 2026 · Artificial Intelligence

Time-Series Forecasting Augmentation: Frequency, Decomposition, and Patch Methods Compared

The article examines the challenges of augmenting time-series forecasting, reviews mainstream techniques—including frequency-domain, decomposition, and patch-based methods—and demonstrates through extensive experiments that Temporal Patch Shuffle (TPS) consistently achieves superior performance across long-term, short-term, and classification tasks.

Data AugmentationTemporal Patch ShuffleTime Series Forecasting
0 likes · 20 min read
Time-Series Forecasting Augmentation: Frequency, Decomposition, and Patch Methods Compared
DataFunTalk
DataFunTalk
May 13, 2026 · Artificial Intelligence

How a 0.5 MB AI Model Tackles Global Supply‑Chain Challenges: Li‑Net in Action

Li‑Net, a 0.5 MB multi‑channel time‑series model co‑developed by SF Technology and Chinese universities, achieves state‑of‑the‑art accuracy with linear‑complexity attention, runs on edge devices, and has been deployed across SF's global supply‑chain for demand forecasting, inventory optimization, and capacity planning, delivering measurable cost reductions.

AIEdge DeploymentLi-Net
0 likes · 4 min read
How a 0.5 MB AI Model Tackles Global Supply‑Chain Challenges: Li‑Net in Action
DataFunSummit
DataFunSummit
May 12, 2026 · Artificial Intelligence

How a 0.5 MB AI Model Tackles Global Supply‑Chain Challenges: Li‑Net Technology and Applications

The article presents Li‑Net, a 0.5 MB lightweight time‑series model co‑developed by SF Technology and universities, accepted at ICDE 2026, which overcomes multi‑channel, non‑stationary, multimodal forecasting difficulties, achieves state‑of‑the‑art accuracy with low latency, and is deployed across SF’s global logistics to improve demand, inventory and capacity planning while cutting costs.

Edge DeploymentLi-NetSupply Chain
0 likes · 4 min read
How a 0.5 MB AI Model Tackles Global Supply‑Chain Challenges: Li‑Net Technology and Applications
Data Party THU
Data Party THU
Apr 30, 2026 · Artificial Intelligence

Time Series Forecasting Augmentation: Frequency, Decomposition, and Patch Techniques

This article reviews why classic classification augmentations fail for forecasting, introduces the essential data‑label consistency requirement, and systematically categorizes effective time‑series augmentation methods—including frequency‑domain (RobustTAD, FreqMask, FreqMix), decomposition (STAug), and patch‑based approaches (WaveMask, WaveMix, Dominant Shuffle, Temporal Patch Shuffle)—backed by extensive experiments on long‑term, short‑term, and classification tasks.

Data AugmentationTemporal Patch ShuffleTime Series Forecasting
0 likes · 20 min read
Time Series Forecasting Augmentation: Frequency, Decomposition, and Patch Techniques
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 14, 2026 · Artificial Intelligence

How Self‑Supervised HINTS Extracts Human Insights from Time Series to Boost Forecast Accuracy

The paper introduces HINTS, a two‑stage self‑supervised framework that leverages Friedkin‑Johnsen opinion dynamics to mine latent human‑driven factors from time‑series residuals, integrates them via attention into state‑of‑the‑art predictors, and demonstrates consistent accuracy gains and interpretability across nine benchmark and real‑world datasets.

Attention MechanismFriedkin-Johnsen modelSelf-Supervised Learning
0 likes · 17 min read
How Self‑Supervised HINTS Extracts Human Insights from Time Series to Boost Forecast Accuracy
AI Explorer
AI Explorer
Apr 4, 2026 · Artificial Intelligence

Google TimesFM: A GPT‑style Foundation Model Redefining Time‑Series Forecasting

Google's open‑source TimesFM model brings pre‑trained, GPT‑like capabilities to time‑series forecasting, offering few‑shot and zero‑shot predictions, extended context length, continuous quantile outputs, and easy integration via a simple PyTorch API for developers across domains.

GooglePyTorchTime Series Forecasting
0 likes · 7 min read
Google TimesFM: A GPT‑style Foundation Model Redefining Time‑Series Forecasting
AI Explorer
AI Explorer
Apr 1, 2026 · Artificial Intelligence

Google Open‑Sources TimesFM: A Foundation Model for Plug‑and‑Play Time‑Series Forecasting

Google’s open‑source TimesFM is a decoder‑only Transformer foundation model that delivers plug‑and‑play time‑series forecasting with zero‑shot accuracy, larger context windows, quantile predictions, and a simple Hugging Face API, making it suitable for retail, energy, finance, monitoring, and IoT use cases.

Hugging FacePyTorchTime Series Forecasting
0 likes · 7 min read
Google Open‑Sources TimesFM: A Foundation Model for Plug‑and‑Play Time‑Series Forecasting
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 20, 2026 · Artificial Intelligence

How Time Distillation Empowers Large Language Models for Time‑Series Forecasting (T‑LLM)

The paper introduces T‑LLM, a time‑distillation framework that transfers predictive behavior from a lightweight teacher model to a general‑purpose LLM, enabling accurate multivariate time‑series forecasting across full‑sample, few‑shot, and zero‑shot settings while eliminating the need for large‑scale pre‑training.

Few-Shot LearningT-LLMTime Series Forecasting
0 likes · 18 min read
How Time Distillation Empowers Large Language Models for Time‑Series Forecasting (T‑LLM)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 13, 2026 · Artificial Intelligence

How ReVol’s Return‑Volatility Normalization Reduces Distribution Shift in Stock Price Prediction

The paper introduces ReVol, a three‑stage framework that normalizes price features, uses an attention‑based estimator to recover return and volatility, and denormalizes predictions, demonstrating consistent improvements of over 0.03 in IC and 0.7 in Sharpe ratio across multiple time‑series models.

Time Series Forecastingattention estimatordeep learning
0 likes · 15 min read
How ReVol’s Return‑Volatility Normalization Reduces Distribution Shift in Stock Price Prediction
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Feb 9, 2026 · Artificial Intelligence

Time‑o1: Overcoming Time‑Series Forecasting Bottlenecks with a Novel Loss Function

The paper identifies two fundamental issues in time‑series forecasting—label autocorrelation bias and task‑scale explosion caused by the standard TMSE loss—and proposes Time‑o1, a PCA‑based orthogonal label transformation that eliminates bias, reduces optimization complexity, and yields consistent performance gains across multiple models and datasets.

NeurIPS 2025PCATime Series Forecasting
0 likes · 12 min read
Time‑o1: Overcoming Time‑Series Forecasting Bottlenecks with a Novel Loss Function
HyperAI Super Neural
HyperAI Super Neural
Jan 23, 2026 · Artificial Intelligence

Weekly AI Paper Digest: New Transformer Advances in Sparsity, Memory, and Reasoning

This article reviews five recent Transformer papers—including Engram's conditional memory, STEM's embedding‑based scaling, SeedFold's biomolecular structure prediction, a critique of Transformers for time‑series forecasting, and reasoning models as societies of thought—highlighting their methods, datasets, and performance gains.

Biomolecular Structure PredictionMemory MechanismsStructural Sparsity
0 likes · 7 min read
Weekly AI Paper Digest: New Transformer Advances in Sparsity, Memory, and Reasoning
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jan 21, 2026 · Artificial Intelligence

Lead–LagNet: Modeling Cross‑Series Lead‑Lag Dependencies for Time‑Series Forecasting

Lead–LagNet addresses three key limitations of existing graph neural networks for multivariate time‑series forecasting—loss of fine‑grained temporal detail, shared weight assumptions, and reduced interpretability—by introducing a sequence preprocessor with a global influence separator and subsequence detector, a subsequence dependency encoder, and a decoupled message‑passing mechanism, achieving superior performance on synthetic benchmarks and S&P 500 market data.

Financial Market PredictionLead‑Lag DependencyLead–LagNet
0 likes · 13 min read
Lead–LagNet: Modeling Cross‑Series Lead‑Lag Dependencies for Time‑Series Forecasting
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 28, 2025 · Artificial Intelligence

Paper Reading: Multi‑Cycle Learning Framework (MLF) for Financial Time‑Series Forecasting

The paper introduces MLF, a multi‑cycle learning framework that integrates three novel modules—inter‑cycle redundancy filtering (IRF), learnable weighted integration (LWI), and multi‑cycle adaptive patch (MAP)—plus a patch‑squeeze component, achieving higher accuracy and efficiency on financial time‑series tasks such as fund‑sales prediction and outperforming strong single‑ and multi‑cycle baselines, with successful deployment in Alipay’s fund inventory system.

Alipay deploymentTime Series Forecastingfinancial AI
0 likes · 16 min read
Paper Reading: Multi‑Cycle Learning Framework (MLF) for Financial Time‑Series Forecasting
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 11, 2025 · Artificial Intelligence

Paper Reading: CoRA – A Multimodal Covariate Adaptation Framework for Time‑Series Foundation Models

CoRA freezes pretrained time‑series foundation models, extracts multimodal covariate embeddings, evaluates their causal relevance with a trainable Granger‑Causal Embedding, and injects them via a zero‑initialized condition module, achieving up to 31.1% MSE reduction across single‑ and multi‑modal forecasting tasks.

Foundation ModelsGranger causal embeddingTime Series Forecasting
0 likes · 12 min read
Paper Reading: CoRA – A Multimodal Covariate Adaptation Framework for Time‑Series Foundation Models
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 30, 2025 · Artificial Intelligence

How TSci Uses LLMs to Automate End‑to‑End Time‑Series Forecasting

The article reviews the TSci framework, an LLM‑driven multi‑agent system that automates data diagnosis, model selection, ensemble forecasting, and report generation for time‑series prediction, achieving up to 38 % lower MAE than LLM baselines and improving report quality across five evaluation dimensions.

Agent FrameworkLLMTSci
0 likes · 10 min read
How TSci Uses LLMs to Automate End‑to‑End Time‑Series Forecasting
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 28, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Nov 22‑28, 2025)

This digest summarizes five recent arXiv papers on AI-driven portfolio optimization and financial time‑series forecasting, covering G‑Learning with GIRL, transfer‑learning strategies, hybrid LSTM‑PPO frameworks, time‑series foundation models, and a KAN versus LSTM performance comparison, highlighting their methods, datasets, and reported Sharpe improvements.

Reinforcement LearningTime Series Forecastingfinancial AI
0 likes · 9 min read
Weekly Quantitative Finance Paper Digest (Nov 22‑28, 2025)
Data Party THU
Data Party THU
Nov 24, 2025 · Artificial Intelligence

Can Post‑Forecast Revision Make Time Series Predictions Truly Reliable?

This article introduces the model‑agnostic PIR framework, which identifies uncertain forecasts and applies local and global post‑hoc revisions to transform average‑accurate time‑series models into systems that deliver stable, instance‑level reliable predictions across diverse real‑world datasets.

AIPIRTime Series Forecasting
0 likes · 8 min read
Can Post‑Forecast Revision Make Time Series Predictions Truly Reliable?
Ctrip Technology
Ctrip Technology
Nov 6, 2025 · Artificial Intelligence

How TripCast Uses Masked 2D Transformers to Revolutionize Travel Time-Series Forecasting

TripCast introduces a masked 2D transformer pre‑training framework that treats travel demand as a two‑dimensional time‑series problem, leveraging time‑patch tokenization, dual masking and RevIN normalization to achieve state‑of‑the‑art forecasting performance on massive real‑world travel data.

2D transformerArtificial IntelligencePretraining
0 likes · 7 min read
How TripCast Uses Masked 2D Transformers to Revolutionize Travel Time-Series Forecasting
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 1, 2025 · Artificial Intelligence

Recent Time-Series Research Summaries (Oct 25‑31 2025)

This article presents concise summaries of five newly released arXiv papers on time‑series forecasting and causal discovery, highlighting each work’s objectives, proposed methods such as FreLE, selective learning, TempoPFN, and DOTS, and the reported experimental improvements.

Time Series Forecastingcausal discoveryselective learning
0 likes · 8 min read
Recent Time-Series Research Summaries (Oct 25‑31 2025)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Oct 18, 2025 · Artificial Intelligence

Time Series Paper Digest (Oct 11‑17 2025): FIRE, CauchyNet, EvoRate, CoRA

From Oct 11‑17 2025, this digest presents four recent AI papers on time‑series forecasting: FIRE introduces a frequency‑domain decomposition with independent amplitude‑phase modeling and adaptive weighting; CauchyNet leverages holomorphic activations for compact, data‑efficient learning; the EvoRate framework quantifies learnability via mutual information; and CoRA adds covariate‑aware adaptation to foundation models, all reporting significant accuracy gains and enhanced interpretability.

AI researchTime Series Forecastingcovariate-aware adaptation
0 likes · 10 min read
Time Series Paper Digest (Oct 11‑17 2025): FIRE, CauchyNet, EvoRate, CoRA
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Oct 11, 2025 · Artificial Intelligence

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.

Time Series ForecastingTransformeranomaly detection
0 likes · 23 min read
Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 23, 2025 · Artificial Intelligence

One-Embedding-Fits-All: Selecting the Best Time-Series Forecasting Model from a Model Zoo

The paper introduces ZooCast, a framework that builds a model zoo of time‑series foundation models and uses a One‑Embedding‑Fits‑All paradigm to embed models and tasks into a unified space, enabling efficient zero‑shot selection that outperforms single models and full‑model ensembles on the GIFT‑Eval benchmark while remaining computationally lightweight.

GIFT-EvalOne-Embedding-Fits-AllTSFM
0 likes · 10 min read
One-Embedding-Fits-All: Selecting the Best Time-Series Forecasting Model from a Model Zoo
Data STUDIO
Data STUDIO
Aug 26, 2025 · Artificial Intelligence

How a Rolling Random Forest Strategy Predicts Bitcoin’s Weekly Direction

This article explains a Python‑based rolling random‑forest classifier that uses a 30‑day training window and selected technical indicators to forecast whether Bitcoin’s price will rise or fall over the next seven days, detailing the methodology, code, back‑test results, and limitations.

BitcoinPythonRandom Forest
0 likes · 7 min read
How a Rolling Random Forest Strategy Predicts Bitcoin’s Weekly Direction
JD Tech Talk
JD Tech Talk
Aug 5, 2025 · Artificial Intelligence

How AI is Revolutionizing Supply Chains: JD.com's Billion‑Scale Time‑Series Model

The article details JD.com’s AI‑driven supply‑chain innovations, including a billion‑scale pure time‑series model, advanced demand forecasting, intelligent inventory selection, and end‑to‑end allocation algorithms that dramatically improve efficiency, cost, and global supply‑chain transformation.

AILogistics OptimizationSupply Chain
0 likes · 10 min read
How AI is Revolutionizing Supply Chains: JD.com's Billion‑Scale Time‑Series Model
JD Retail Technology
JD Retail Technology
Jul 31, 2025 · Artificial Intelligence

How AI is Revolutionizing Supply Chains: JD.com’s Billion‑Parameter Time‑Series Model

At the 2025 AI Innovation & Entrepreneurship Conference in Hangzhou, JD.com’s chief scientists unveiled a billion‑parameter time‑series large model and end‑to‑end inventory algorithms that dramatically boost demand forecasting, dynamic allocation, and overall supply‑chain efficiency, illustrating how AI can transform global logistics networks.

AIData‑Driven Decision MakingLogistics Optimization
0 likes · 10 min read
How AI is Revolutionizing Supply Chains: JD.com’s Billion‑Parameter Time‑Series Model
JD Tech
JD Tech
Apr 30, 2025 · Artificial Intelligence

TimeHF: A Billion‑Scale Time Series Forecasting Model Guided by Human Feedback

The JD Supply Chain algorithm team introduces TimeHF, a billion‑parameter time‑series large model that leverages RLHF to boost demand‑forecast accuracy by over 10%, detailing dataset construction, the PCTLM architecture, a custom RLHF framework (TPO), and extensive SOTA experimental results.

RLHFSupply ChainTime Series Forecasting
0 likes · 10 min read
TimeHF: A Billion‑Scale Time Series Forecasting Model Guided by Human Feedback
JD Cloud Developers
JD Cloud Developers
Apr 11, 2025 · Artificial Intelligence

How a Billion-Parameter Time Series Model Beats GPT4TS: The PCTLM Breakthrough

This article introduces PCTLM, a pioneering billion‑parameter pure time‑series large model that outperforms existing solutions like GPT4TS across multiple benchmarks, detailing its massive high‑quality dataset, novel patch‑based architecture, and a tailored RLHF framework (TPO) that enhances zero‑shot forecasting accuracy.

PCTLMRLHFTime Series Forecasting
0 likes · 11 min read
How a Billion-Parameter Time Series Model Beats GPT4TS: The PCTLM Breakthrough
JD Tech Talk
JD Tech Talk
Apr 11, 2025 · Artificial Intelligence

A Billion-Scale Pure Time Series Large Model: PCTLM with SFT and TPO for Forecasting

This article presents a pioneering billion‑parameter pure time‑series large model (PCTLM) trained on a 1.5‑billion‑sample dataset, introduces a novel RLHF framework (TPO) for time‑series forecasting, and demonstrates state‑of‑the‑art performance across multiple public benchmarks, surpassing existing models such as GPT4TS.

PCTLMRLHFTPO
0 likes · 11 min read
A Billion-Scale Pure Time Series Large Model: PCTLM with SFT and TPO for Forecasting
AntTech
AntTech
Mar 5, 2025 · Artificial Intelligence

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

Pyraformer introduces a pyramidal attention mechanism that captures long-range dependencies in time-series data with linear time and space complexity, achieving state-of-the-art forecasting accuracy on multiple real-world datasets while reducing computational cost, as demonstrated in extensive ICLR-2022 experiments.

ICLR 2022PyraformerTime Series Forecasting
0 likes · 11 min read
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Oct 8, 2024 · Artificial Intelligence

Two NIRC Papers Accepted at NeurIPS 2024: FM-Delta Compression and GLAFF Forecasting

The Beijing University of Posts and Telecommunications' Network Intelligent Research Center (NIRC) had two papers accepted to NeurIPS 2024, presenting FM-Delta, a lossless compression technique that halves storage and cuts cloud costs by over 40%, and GLAFF, a global‑local fusion framework that markedly improves the robustness of time‑series forecasting across multiple domains.

AI researchFM-DeltaGLAFF
0 likes · 8 min read
Two NIRC Papers Accepted at NeurIPS 2024: FM-Delta Compression and GLAFF Forecasting
Ctrip Technology
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 NetworkPythonSCNN
0 likes · 30 min read
Structured Components-based Neural Network (SCNN) for Multivariate Time Series Forecasting: Theory, Implementation, and Business Application
Model Perspective
Model Perspective
Jul 24, 2024 · Fundamentals

Boost Time Series Forecast Accuracy with the Grey‑Markov Hybrid Model

This article introduces the Grey‑Markov hybrid model, explains its theoretical foundations, outlines step‑by‑step modeling procedures, and demonstrates its superior forecasting performance on a consumer price index (CPI) case study, achieving a significant reduction in prediction error.

CPI PredictionGrey ModelHybrid Model
0 likes · 7 min read
Boost Time Series Forecast Accuracy with the Grey‑Markov Hybrid Model
Alimama Tech
Alimama Tech
Jun 21, 2024 · Artificial Intelligence

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

CausalMMM introduces an encoder‑decoder framework that automatically discovers heterogeneous, interpretable causal graphs among advertising channels while modeling temporal decay and saturation, using Granger‑based variational inference, and achieves over 5.7% improvement in causal structure learning and significant GMV prediction gains on Alibaba’s data.

Time Series Forecastingmarketing mix modelingvariational inference
0 likes · 16 min read
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
DeWu Technology
DeWu Technology
May 31, 2024 · Artificial Intelligence

In-depth Analysis of Prophet Time Series Forecasting Model

The article offers a thorough examination of Facebook’s Prophet forecasting model, detailing its additive decomposition of trend, seasonality, holidays and regressors, the underlying Bayesian inference via Stan, the full training‑and‑prediction pipeline, data‑normalization tricks, uncertainty estimation, and practical source‑code insights for e‑commerce applications.

Prophet modelSource Code AnalysisStan framework
0 likes · 21 min read
In-depth Analysis of Prophet Time Series Forecasting Model
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
May 30, 2024 · Artificial Intelligence

How Pathformer Redefines Multi-Scale Time Series Forecasting with Adaptive Pathways

Pathformer, a new multi‑scale Transformer model introduced by Alibaba Cloud’s big‑data team and East China Normal University, leverages adaptive pathways to jointly model time resolution and time distance, achieving state‑of‑the‑art forecasting performance and strong generalization across cloud resource workloads and public datasets.

Multi-ScaleTime Series ForecastingTransformer
0 likes · 7 min read
How Pathformer Redefines Multi-Scale Time Series Forecasting with Adaptive Pathways
Ctrip Technology
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.

AutoformerBusiness MetricsInformer
0 likes · 18 min read
Time Series Forecasting of Key Business Indicators: Methods, Models, and Practical Deployment
DataFunSummit
DataFunSummit
Oct 3, 2023 · Artificial Intelligence

Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, Algorithm Practice, and Future Outlook

This article presents a comprehensive case study of NIO's Power swap‑station ecosystem, detailing the business context, key forecasting challenges, the evolution from classical statistical models to deep‑learning architectures with specialized embeddings, and the practical outcomes and future plans for improving prediction accuracy.

Electric VehicleNIO PowerTime Series Forecasting
0 likes · 16 min read
Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, Algorithm Practice, and Future Outlook
DataFunTalk
DataFunTalk
Jul 13, 2023 · Artificial Intelligence

Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, and Algorithm Practice

This article presents NIO's smart energy service platform, focusing on the NIO Power swap‑station business and detailing how time‑series forecasting is applied to predict demand, addressing complex seasonality, holiday drift, growth and competition, and describing the underlying machine‑learning and deep‑learning models and system architecture.

NIOTime Series Forecastingembedding
0 likes · 16 min read
Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, and Algorithm Practice
DataFunSummit
DataFunSummit
Feb 1, 2023 · Artificial Intelligence

Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting

The paper proposes clustering thousands of related time series and training separate global LSTM models for each cluster, showing that this reduces heterogeneity, leverages shared information, and improves forecasting accuracy compared to individual models, with extensive experiments on CIF2016 and NN5 datasets.

ClusteringLSTMRNN
0 likes · 33 min read
Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting
Ctrip Technology
Ctrip Technology
Oct 20, 2022 · Artificial Intelligence

Mid‑ and Long‑Term Monthly Hotel Room‑Night Forecasting under Pandemic Conditions

This article presents a pandemic‑aware method for predicting national hotel monthly room‑nights over the next six months, detailing data augmentation, feature engineering, LSTM and SARIMA‑LASSO modeling, scenario‑based risk assessment, and evaluation results that demonstrate accurate forecasts despite COVID‑19 disruptions.

AILSTMPandemic Impact
0 likes · 14 min read
Mid‑ and Long‑Term Monthly Hotel Room‑Night Forecasting under Pandemic Conditions
Alibaba Cloud Native
Alibaba Cloud Native
Oct 3, 2022 · Cloud Native

Can AHPA Predict Kubernetes Scaling Before Load Spikes?

This article introduces the Advanced Horizontal Pod Autoscaler (AHPA), explains its three‑stage architecture of data collection, prediction, and scaling, details the RobustScaler forecasting algorithm and CRD‑based deployment, and evaluates its ability to proactively and reactively adjust pod counts with high robustness.

AutoscalingCRDKubernetes
0 likes · 13 min read
Can AHPA Predict Kubernetes Scaling Before Load Spikes?
Model Perspective
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.

ProphetPythonTime Series Forecasting
0 likes · 12 min read
How Prophet Implements Time Series Decomposition and Trend Modeling
DataFunTalk
DataFunTalk
Jun 16, 2022 · Artificial Intelligence

BigBang Transformer (BBT): A 1‑Billion‑Parameter Financial Pre‑trained Language Model with Time‑Series‑Text Cross‑Modal Architecture

The BigBang Transformer (BBT) is a 1‑billion‑parameter financial pre‑trained language model that combines text and time‑series data in a cross‑modal Transformer architecture, achieving up to 10% higher downstream accuracy than T5‑scale models and demonstrating strong performance on financial NLP tasks, time‑series forecasting, and multi‑factor investment strategies.

Artificial IntelligenceFinancial NLPTime Series Forecasting
0 likes · 19 min read
BigBang Transformer (BBT): A 1‑Billion‑Parameter Financial Pre‑trained Language Model with Time‑Series‑Text Cross‑Modal Architecture
NetEase Yanxuan Technology Product Team
NetEase Yanxuan Technology Product Team
May 5, 2022 · Artificial Intelligence

Time Series Forecasting Algorithm System in E-commerce: Practice and Applications at NetEase Yanxuan

NetEase Yanxuan built an end‑to‑end time‑series forecasting system for e‑commerce that integrates rich user, product, business and external features with a suite of statistical, machine‑learning and deep‑learning models, delivers predictions via a Tornado‑based service for thousands of SKUs, warehouses, advertising and app traffic, and shows that simpler models like XGBoost often outperform complex deep nets while interpretability and external shocks remain key challenges.

Data ScienceSales PredictionTime Series Forecasting
0 likes · 10 min read
Time Series Forecasting Algorithm System in E-commerce: Practice and Applications at NetEase Yanxuan
AntTech
AntTech
Apr 27, 2022 · Artificial Intelligence

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

The paper introduces Pyraformer, a low‑complexity pyramidal‑attention Transformer that captures multi‑scale temporal dependencies with linear time‑space complexity, achieving superior single‑step and long‑range forecasting performance on real‑world datasets while supporting green‑computing capacity management.

PyraformerTime Series ForecastingTransformer
0 likes · 14 min read
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
GuanYuan Data Tech Team
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.

AutoMLGBDTProphet
0 likes · 27 min read
Mastering Time Series Forecasting: From Moving Averages to Transformers
Tencent Advertising Technology
Tencent Advertising Technology
Oct 29, 2020 · Artificial Intelligence

Large-Scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework

This article discusses a deep spatial-temporal tensor factorization framework for large-scale user visits understanding and forecasting, addressing challenges in advertising inventory prediction and demonstrating significant improvements over traditional methods.

Artificial IntelligenceData ScienceTime Series Forecasting
0 likes · 9 min read
Large-Scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework
Suning Technology
Suning Technology
Aug 29, 2020 · Artificial Intelligence

How AI Powers Large‑Scale Time Series Forecasting and Root‑Cause Analysis

This article describes Suning's AI‑driven end‑to‑end solution for massive time‑series monitoring, anomaly detection, forecasting with DeepAR, MQ‑RNN, MQ‑CNN, ensemble methods, root‑cause localization using Hotspot and Monte‑Carlo Tree Search, and the evolution of its large‑scale log analytics platform.

Log AnalyticsTime Series Forecastinganomaly detection
0 likes · 17 min read
How AI Powers Large‑Scale Time Series Forecasting and Root‑Cause Analysis
Bitu Technology
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
dbaplus Community
dbaplus Community
Mar 9, 2020 · Artificial Intelligence

How LSTM‑Powered Real‑Time Alerting with Spark Streaming Boosts Ops Efficiency

This article details a deep‑learning‑driven, real‑time alert system that combines TensorFlow LSTM time‑series forecasting with Spark Streaming to achieve high‑coverage, low‑latency anomaly detection for large‑scale data‑ops environments, including data preprocessing, metric classification, model training, and deployment pipelines.

AI OpsLSTMSpark Streaming
0 likes · 18 min read
How LSTM‑Powered Real‑Time Alerting with Spark Streaming Boosts Ops Efficiency
Architecture Digest
Architecture Digest
Jan 28, 2020 · Artificial Intelligence

Predicting COVID-19 Cases Using LSTM Based on SARS Data: Methodology and Evaluation

This article investigates whether a short‑term time‑series algorithm, specifically an LSTM model trained on limited SARS data, can predict and assess COVID‑19 case numbers, describing data collection, model training, experimental validation, error analysis, and practical implications of the findings.

COVID-19 predictionData AnalysisEpidemiology
0 likes · 11 min read
Predicting COVID-19 Cases Using LSTM Based on SARS Data: Methodology and Evaluation
Tencent Advertising Technology
Tencent Advertising Technology
May 8, 2019 · Artificial Intelligence

Experience Sharing of the 2019 Tencent Advertising Algorithm Competition – Week 1 Champion’s Insights

The week‑1 champion of the 2019 Tencent Advertising Algorithm Competition shares practical experience on data cleaning, feature engineering, model selection (including LightGBM and deep learning), validation strategies, and tips for handling massive ad exposure logs to achieve high SMAPE and monotonicity scores.

AdvertisingAlgorithm CompetitionLightGBM
0 likes · 6 min read
Experience Sharing of the 2019 Tencent Advertising Algorithm Competition – Week 1 Champion’s Insights
Tencent Cloud Developer
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
Using Facebook Prophet for Time Series Forecasting: Predicting Tencent Cloud Database Storage Trends
High Availability Architecture
High Availability Architecture
May 9, 2018 · Artificial Intelligence

Building a Data‑Driven Intelligent Operations (AIOps) Platform: Architecture, Core Scenarios, and Open‑Source Tools

This article presents a comprehensive guide to constructing a data‑driven AIOps platform, detailing its architecture, core components such as time‑series forecasting, anomaly detection, and pattern clustering, and recommending open‑source projects and practical considerations for implementing intelligent operations in enterprises.

AIOpsLog ClusteringTime Series Forecasting
0 likes · 13 min read
Building a Data‑Driven Intelligent Operations (AIOps) Platform: Architecture, Core Scenarios, and Open‑Source Tools