AIBullisharXiv – CS AI · Jun 257/10
🧠MacroLens is a new financial reasoning benchmark that combines price history, accounting fundamentals, macroeconomic data, and news text to evaluate AI models on seven financial tasks across 4,416 U.S. small- and micro-cap stocks. The dataset addresses critical evaluation challenges unique to finance and tests 19 methods ranging from heuristics to frontier LLMs, providing a standardized tool for developing contextual financial AI systems.
🏢 Hugging Face
AIBullisharXiv – CS AI · Jun 17/10
🧠Researchers introduce Parallel Variational Monte Carlo (PVMC), a novel training method for deep state space models that combines strengths of variational and sequential Monte Carlo approaches. The technique achieves comparable or superior performance to existing methods while running 10x faster, addressing a critical scalability bottleneck in training complex temporal models.
AIBullisharXiv – CS AI · May 287/10
🧠Researchers benchmark Liquid Neural Networks (LNNs) against traditional LSTMs across four sequential data domains, finding that LNNs deliver superior parameter efficiency and robustness in handling sparse, temporal data—particularly valuable for clinical applications. The study demonstrates LNNs' continuous-time modeling approach outperforms discrete-step RNNs when data is missing or irregularly sampled, suggesting significant implications for real-world AI deployment in healthcare and edge computing.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers introduce FactoryNet, the first universal pretraining dataset for industrial time-series data containing 51M datapoints across 23k task executions in robotic and machining domains. The dataset employs a novel S-E-F-C schema enabling cross-embodiment transfer and efficient anomaly detection, advancing toward industrial foundation models.
🏢 Meta
AIBullisharXiv – CS AI · May 97/10
🧠Researchers introduce VeriTime, a framework that enhances large language models for time series analysis through synthetic data generation, intelligent data scheduling, and specialized reinforcement learning. The approach enables smaller models (3B-4B parameters) to match or exceed the reasoning capabilities of larger proprietary LLMs on time series tasks.
AIBullisharXiv – CS AI · Apr 207/10
🧠Researchers introduce EVIL, an LLM-guided evolutionary approach that discovers interpretable Python algorithms for zero-shot inference on time series and event sequences without traditional neural network training. The evolved algorithms match or exceed deep learning performance while remaining transparent and significantly faster, demonstrating a novel paradigm for dynamical systems inference.
AIBullisharXiv – CS AI · Mar 177/10
🧠Researchers introduce EARCP, a new ensemble architecture for AI that dynamically weights different expert models based on performance and coherence. The system provides theoretical guarantees with sublinear regret bounds and has been tested on time series forecasting, activity recognition, and financial prediction tasks.
AINeutralarXiv – CS AI · Mar 127/10
🧠Researchers applied sparse autoencoders to analyze Chronos-T5-Large, a 710M parameter time series foundation model, revealing how different layers process temporal data. The study found that mid-encoder layers contain the most causally important features for change detection, while early layers handle frequency patterns and final layers compress semantic concepts.
AINeutralarXiv – CS AI · Mar 57/10
🧠Researchers propose a new evaluation methodology for temporal deep learning that controls for effective sample size rather than raw sequence length. Their analysis of Temporal Convolutional Networks on time series data shows that stronger temporal dependence can actually improve generalization when properly evaluated, contradicting results from standard evaluation methods.
AIBullisharXiv – CS AI · Mar 57/10
🧠IBM researchers introduce TSPulse, an ultra-lightweight pre-trained AI model with only 1M parameters that achieves state-of-the-art performance in time-series analysis tasks. The model uses disentangled representations across temporal, spectral, and semantic views, delivering significant performance gains of 20-50% across multiple diagnostic tasks while being 10-100x smaller than competing models.
🏢 Hugging Face
AIBullisharXiv – CS AI · Mar 46/103
🧠Researchers developed cPNN (Continuous Progressive Neural Networks), a new AI architecture that handles evolving data streams with temporal dependencies while avoiding catastrophic forgetting. The system addresses concept drift in time series data by combining recurrent neural networks with progressive learning techniques, showing quick adaptation to new concepts.
AINeutralarXiv – CS AI · Mar 47/103
🧠Researchers introduce TimeGS, a novel time series forecasting framework that reimagines prediction as 2D generative rendering using Gaussian splatting techniques. The approach addresses key limitations in existing methods by treating future sequences as continuous latent surfaces and enforcing temporal continuity across periodic boundaries.
AIBullisharXiv – CS AI · Mar 37/102
🧠Researchers introduce Verbal Technical Analysis (VTA), a framework that combines Large Language Models with time-series analysis to produce interpretable stock forecasts. The system converts stock price data into textual annotations and uses natural language reasoning to achieve state-of-the-art forecasting accuracy across U.S., Chinese, and European markets.
AINeutralarXiv – CS AI · Jun 255/10
🧠Researchers propose Self-Modulating Quantum Fast Weight Programmers (QFWP), an advancement in quantum machine learning that improves sequential data processing through adaptive modulation of fast-weight updates and memory. The approach demonstrates enhanced convergence stability and prediction performance across various quantum configurations, positioning quantum computing as increasingly viable for time-series analysis applications.
AINeutralarXiv – CS AI · Jun 255/10
🧠Researchers propose Recursive QLSTM, a quantum machine learning model that extends quantum long short-term memory networks through recursive metacore-based constructions for improved sequential data processing. The model demonstrates enhanced temporal information propagation across variable input sequence lengths, offering a flexible framework for quantum computing applications in time-series analysis.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers introduce CATCH, a novel framework for detecting anomalies in multivariate time series data using frequency patching and channel-aware mechanisms. The method achieves state-of-the-art performance across 22 datasets by improving detection of fine-grained frequency patterns while identifying relevant channel correlations through a Channel Fusion Module.
AINeutralarXiv – CS AI · Jun 116/10
🧠Researchers propose Multi-Rate Mixture-of-Experts (MR-MoE), a framework that enhances Liquid Neural Networks for time-series modeling by deploying multiple experts operating at different time scales with adaptive gating. The approach combines continuous-time dynamics, multi-scale decomposition, and attention mechanisms to outperform traditional RNNs and monolithic LNNs on complex multivariate time-series tasks.
AINeutralarXiv – CS AI · Jun 116/10
🧠Researchers introduce ERTS, an explainability-based training method that reduces computational costs for ECG classification by using attention map quality to identify which training samples are genuinely informative versus noisy. The approach demonstrates consistent performance improvements across multiple datasets while significantly lowering training expenses, offering practical efficiency gains for resource-constrained healthcare environments.
AINeutralarXiv – CS AI · Jun 116/10
🧠A comprehensive survey examines how large language models can reason about time series data through three structural topologies: direct reasoning, linear chain reasoning, and branch-structured reasoning. The research organizes methods across objectives including analysis, explanation, causal inference, and generation, emphasizing the need for evaluation practices that maintain evidence visibility and temporal alignment while balancing computational cost against reliability and reproducibility.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers introduce UniTok, a universal tokenizer that converts continuous time series data into discrete tokens, enabling UniTok-FM—a foundation model pretrained via next-token prediction. This unified approach supports forecasting, generation, and classification tasks without task-specific modifications, achieving competitive performance with specialized models while enabling zero-shot and few-shot inference capabilities.
AIBullisharXiv – CS AI · Jun 106/10
🧠Researchers demonstrate a Mojo-based k-d tree algorithm that achieves 17.5-43.5× speedup over existing implementations for nearest-neighbor learning on high-frequency financial time series. The approach enables financial AI systems to process larger datasets while maintaining real-time latency requirements for trading and risk management applications.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers propose InA-Probe, a novel framework that enables Large Language Models to perform time series forecasting through instruction-aware active probing rather than passive alignment. The method achieves up to 37% error reduction on cross-domain benchmarks and demonstrates strong generalization and zero-shot transfer capabilities.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers introduce SRT (Super-Resolution for Time Series), a novel AI framework using disentangled rectified flow to reconstruct high-resolution temporal data from low-resolution inputs. The method decomposes time series into trend and seasonal components, employs implicit neural representations, and includes a cross-resolution attention mechanism, with a scaled pre-trained version (SRT-large) demonstrating strong zero-shot capabilities across multiple datasets.
AINeutralarXiv – CS AI · Jun 96/10
🧠A comprehensive survey reviews the emergence of large foundation models adapted for analyzing time series and spatio-temporal data, categorizing approaches into two groups: models for time series analysis (LM4TS) and spatio-temporal data mining (LM4STD). The research consolidates recent advances in applying large language models and foundation models to temporal data across diverse domains, establishing a foundation for understanding how AI systems can process dynamic, sensor-generated information at scale.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers have developed a large-scale benchmark dataset for evaluating causal inference methods in epidemic time-series prediction under dynamic interventions. Using calibrated agent-based models grounded in real-world U.S. county data, the benchmark enables testing of causal inference techniques across static and time-varying treatment scenarios with verifiable counterfactual outcomes.
