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TAG · #TIME-SERIES

Time Series coverage.

Every story in the WeSearch catalog tagged with #time-series, chronological, with view counts. Subscribe to the per-tag RSS feed to follow this topic in your reader of choice.

17 stories tagged with #time-series, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.

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#ml6#ai5#forecasting3#time-series-forecasting1#research1#automation1#programming1#data-contamination1
ARXIV CS.AI

From Long News to Accurate Forecast: Importance-Aware Fusion and PRM-Guided Reflection for Time Series Forecasting

Incorporating news into time series forecasting is appealing because news can reveal abrupt exogenous events that historical values alone cannot recover. However, existing LLM-base…

21 views ·
#artificial intelligence#forecasting#machine learning
ARXIV CS.AI

TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning

Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs…

16 views ·
#artificial intelligence#data quality#machine learning
DEV.TO (TOP)

I Built CausalLens — A Free, Open-Source Causal Impact Calculator for Time Series (5 Methods, Zero Setup)

I want to show you a tool I just open-sourced. It's called CausalLens, and it answers one specific...…

16 views ·
#opensource#datascience#statistics
TOWARDS DATA SCIENCE

Five Questions About Chronos-2, the Time Series Foundation Model

Part 1: A practitioner's walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting.…

17 views ·
#machine learning#forecasting
ARXIV CS.AI

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models

Time series foundation models (TSFMs) are increasingly pretrained on large corpora, raising concerns that evaluation datasets may have been exposed during pretraining and thus yiel…

14 views ·
#machine learning#data contamination
MEDIUM

LLM Driven AutoForecasting with Sktime's `Craft()`

AutoML is relying in many cases on some kind of grid searches. This is expensive. However, if humans are selecting hyperparameters, they……

17 views ·
#machine learning#automation
ARXIV CS.AI

AION: Next-Generation Tasks and Practical Harness for Time Series

Time series research is moving beyond fixed forecasting benchmarks toward realistic tasks that combine prediction, contextual reasoning, tool use, and structured decision support. …

16 views ·
#artificial intelligence#research
ARXIV CS.AI

CALAD: Channel-Aware contrastive Learning for multivariate time series Anomaly Detection

Multivariate time series anomaly detection has become increasingly important in real-world applications, where labeled data are often scarce. Many existing approaches rely on unsup…

13 views ·
#machine learning#anomaly detection#artificial intelligence
ARXIV CS.AI

PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows

Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on deterministic models. However, real-…

13 views ·
#machine learning#forecasting#artificial intelligence
ARXIV CS.AI

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting

Effectively modeling non-stationary dynamics in probabilistic multivariate time series(MTS) forecasting requires balancing expressiveness with robustness. Existing parametric appro…

12 views ·
#machine learning#artificial intelligence#time series forecasting
ARXIV CS.AI

Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding

Real-world time series come with text: metadata, descriptions, news, reports. Yet time series foundation models process numerical sequences in isolation, and the multimodal text-an…

15 views ·
#machine learning#artificial intelligence#natural language processing
ARXIV CS.AI

LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series

Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer arises because language pretraining precond…

16 views ·
#machine learning#artificial intelligence
ARXIV CS.AI

Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting

Non-stationary time series forecasting is challenged by evolving distribution shifts that static models struggle to capture. While Mixture-of-Experts (MoE) architectures offer a pr…

17 views ·
#machine learning#forecasting
ARXIV CS.AI

INSIGHTS: Demonstration-Based Summaries of Time Series Predictors

Explainability methods have progressed rapidly, but global explanations for time-series models remain underdeveloped, with most approaches focusing on local, instance-level attribu…

14 views ·
#machine learning#artificial intelligence#data science
ARXIV CS.AI

POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection

Existing Multivariate Time Series Anomaly Detection (MTSAD) frameworks increasingly rely on integrating Graph Neural Networks (GNNs) with sequence models to capture complex spatio-…

12 views ·
#artificial intelligence#machine learning#anomaly detection
PHORONIX

More Intel Open-Source Projects Formally Sunset: BigDL Time Series Toolkit & Others

Yet more open-source Intel software projects have been formally archived…

17 views ·
#intel#open-source#software
R/TELEVISION

What are your absolutely perfect, lightning-in-a-bottle, all time series/miniseries? I’ll start.

13 views ·