time-series

This paper proposes a Gated Bottleneck Latent ODE combined with Multi-Path Just-In-Time Fine-Tuning and Raman data fusion to improve multi-day forecasting of mammalian cell culture processes, achieving better performance on real bioreactor data.

This paper proposes Continuous Power Forecasting, treating power forecasting as a continual learning problem to handle nonstationary conditions. It evaluates six CL approaches on real-world datasets, showing benefits in adaptation and mitigating catastrophic forgetting.

MacroLens is a new multi-task benchmark for contextual financial reasoning that jointly evaluates price history, accounting fundamentals, macroeconomic regimes, and textual data across 4,416 U.S. small- and micro-cap equities. It includes seven tasks, 1,130 macroeconomic events, and evaluations of 19 methods, aiming to fill a gap in financial AI evaluation.

The paper proposes RAVEN, a Mixture-of-Experts framework that adaptively determines temporal context windows for each input sample to handle non-stationary financial time series. It achieves state-of-the-art performance on financial and traffic benchmarks.

Proposes GRACE, a method that combines constraint-based skeleton with gated refinement using L0 regularization for efficient and accurate causal edge discovery in high-dimensional time series. It outperforms existing methods in F1 and speed, demonstrated on synthetic and real-world river flow data.

Amazon open-sourced Chronos, a time-series forecasting model that predicts out of the box without training or feature engineering, treating forecasting like language models treat text.

This paper argues that time series modeling should incorporate a dynamical systems perspective to improve understanding and prediction of complex temporal data.

This paper introduces CADE, a framework for time-series question answering that maps each timestep directly into the LLM embedding space and uses a one-directional supervised contrastive loss to align time-series representations with frozen text anchors, outperforming existing baselines on the Time-MQA benchmark.

This paper introduces regime-stratified evaluation for time series foundation models, revealing that aggregate metrics hide severe failures during traffic regime transitions, and proposes bimodal mixture augmentation to improve coverage while preserving overall accuracy.

Google has released TimesFM, a time series forecasting model trained on 100 billion real-world time series data, supporting zero-shot prediction. It is free, open-source, and can run locally on ordinary computers.

GreptimeDB v1.1 introduces online repartitioning for existing tables, incremental Flow reads, a semantic layer for LLMs, and stability improvements.

Google has released TimesFM, an AI model for zero-shot time series forecasting, trained on 100 billion real data points, free and open-source.

Introduces IRTS-ToolBench, a benchmark of 1,700 questions for evaluating LLMs and AI agents on irregular time series question answering via tool-grounded reasoning, covering 10 task types across 13 domains.

Introduces TimeMoDE, a framework combining Diffusion Transformers with Mixture-of-Experts for generating realistic time series under data scarcity, using pre-training on multi-domain datasets and domain prompts to handle domain-specific features and diffusion timestep signals for adaptive denoising.

This paper examines whether ML models can beat the random walk benchmark in forecasting USD/CAD exchange rates, finding that only linear regression statistically outperforms the naive model, with SHAP analysis showing short-term lags dominate predictions.

Forgis Labs presents a family of foundation models for time series sensor data in industrial settings, with five papers accepted to ICML 2026 workshops, enabling event prediction and natural language explanation from raw sensor streams.

GreptimeDB v1.1.0 is released, offering up to 97% faster PromQL queries, 20-40% lower overall query times, and up to 4.5x improvement on TSBS scan-heavy queries, along with online repartitioning for existing tables.

This paper proposes ORCA, a method for black-box online adaptation of time series foundation models by learning the context of predictive errors. It demonstrates effectiveness across five TSFMs and eight datasets, addressing the challenge of adapting closed-source API-based models.

Proposes a spectral learning method for stochastic nonlinear dynamical systems using deep feature spaces and an operator-based latent state-space model, demonstrating stable performance in forecasting and filtering tasks.

This paper proposes a falsifiable applicability criterion for a training-free, fixed-length descriptor for multivariate time series based on time-lagged spectral embeddings, showing when it can be expected to work and validating it on multiple benchmarks.