Latest 15 Papers - May 20, 2025

This article provides an overview of the latest 15 papers in the fields of Time Series, Spatio Temporal, Time Series Imputation, Irregular Time Series, Diffusion Model, and Graph Neural Networks. The papers are listed in the respective sections below.

Time Series

Time series analysis is a crucial aspect of machine learning, and researchers have been working on various techniques to improve the accuracy of time series forecasting. Here are some of the latest papers in this field:

• An Empirical Bayes approach to ARX Estimation: This paper proposes an empirical Bayes approach to estimate the parameters of an autoregressive (AR) model with exogenous variables (ARX). The approach is based on the idea of using a prior distribution for the parameters and updating it using the data.
• TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents: This paper introduces a new benchmark for time series machine learning, called TimeSeriesGym. The benchmark provides a scalable and flexible framework for evaluating the performance of time series machine learning models.
• Level Generation with Quantum Reservoir Computing: This paper proposes a new approach to level generation using quantum reservoir computing. The approach is based on the idea of using a quantum reservoir to learn the patterns in the data.
• Performance of Zero-Shot Time Series Foundation Models on Cloud Data: This paper evaluates the performance of zero-shot time series foundation models on cloud data. The results show that the models can achieve high accuracy on cloud data.
• True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics: This paper proposes a new approach to zero-shot inference of dynamical systems that preserve long-term statistics. The approach is based on the idea of using a prior distribution for the parameters and updating it using the data.
• Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment: This paper proposes a new approach to enhancing large language models (LLMs) for time series forecasting. The approach is based on the idea of using a structure-guided cross-modal alignment to align the LLMs with the time series data.
• RIFLES: Resource-effIcient Federated LEarning via Scheduling: This paper proposes a new approach to resource-efficient federated learning via scheduling. The approach is based on the idea of using a scheduling algorithm to allocate the resources efficiently.
• panelPomp: Analysis of Panel Data via Partially Observed Markov Processes in R: This paper proposes a new approach to analyzing panel data using partially observed Markov processes in R. The approach is based on the idea of using a Markov process to model the panel data.
• Toward Relative Positional Encoding in Spiking Transformers: This paper proposes a new approach to relative positional encoding in spiking transformers. The approach is based on the idea of using a relative positional encoding to improve the performance of the spiking transformers.
• Time series saliency maps: explaining models across multiple domains: This paper proposes a new approach to explaining models across multiple domains using time series saliency maps. The approach is based on the idea of using a saliency map to highlight the important features in the data.
• Unifying concepts in information-theoretic time-series analysis: This paper proposes a new approach to unifying concepts in information-theoretic time-series analysis. The approach is based on the idea of using a unified framework to analyze the time series data.
• Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data: This paper proposes a new approach to exploring neural Granger causality with xLSTMs. The approach is based on the idea of using an xLSTM to model the temporal dependencies in the data.
• Testing procedures based on maximum likelihood estimation for Marked Hawkes processes: This paper proposes a new approach to testing procedures based on maximum likelihood estimation for marked Hawkes processes. The approach is based on the idea of using a maximum likelihood estimation to test the hypotheses.
• TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis: This paper proposes a new approach to dual space tiny pre-trained models for rapid time-series analysis. The approach is based on the idea of using a dual space to improve the performance of the pre-trained models.
• Agent Performing Autonomous Stock Trading under Good and Bad Situations: This paper proposes a new approach to an agent performing autonomous stock trading under good and bad situations. The approach is based on the idea of using a reinforcement learning algorithm to train the agent.

Spatio Temporal

Spatio-temporal analysis is a crucial aspect of machine learning, and researchers have been working on various techniques to improve the accuracy of spatio-temporal forecasting. Here are some of the latest papers in this field:

• Hybrid Voting-Based Task Assignment in Modular Construction Scenarios: This paper proposes a new approach to hybrid voting-based task assignment in modular construction scenarios. The approach is based on the idea of using a voting algorithm to assign the tasks.
• Just Dance with $π$! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection: This paper proposes a new approach to just dance with $π$! A poly-modal inductor for weakly-supervised video anomaly detection. The approach is based on the idea of using a poly-modal inductor to detect the anomalies.
• Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation: This paper proposes a new approach to time-frequency-based attention cache memory model for real-time speech separation. The approach is based on the idea of using a time-frequency-based attention to improve the performance of the speech separation.
• Scene-Text Grounding for Text-Based Video Question Answering: This paper proposes a new approach to scene-text grounding for text-based video question answering. The approach is based on the idea of using a scene-text grounding to improve the performance of the question answering.
• EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting: This paper proposes a new approach to EpiLLM: Unlocking the potential of large language models in epidemic forecasting. The approach is based on the idea of using a large language model to improve the performance of the epidemic forecasting.
• Super-Resolution Generative Adversarial Networks based Video Enhancement: This paper proposes a new approach to super-resolution generative adversarial networks based video enhancement. The approach is based on the idea of using a super-resolution generative adversarial network to improve the performance of the video enhancement.
• DYNUS: Uncertainty-aware Trajectory Planner in Dynamic Unknown Environments: This paper proposes a new approach to DYNUS: Uncertainty-aware trajectory planner in dynamic unknown environments. The approach is based on the idea of using an uncertainty-aware trajectory planner to improve the performance of the trajectory planning.
• Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach: This paper proposes a new approach to does vector quantization fail in spatio-temporal forecasting? Exploring a differentiable sparse soft-vector quantization approach. The approach is based on the idea of using a differentiable sparse soft-vector quantization to improve the performance of the spatio-temporal forecasting.
• AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data: This paper proposes a new approach to AFCL: Analytic federated continual learning for spatio-temporal invariance of non-IID data. The approach is based on the idea of using an analytic federated continual learning to improve the performance of the spatio-temporal invariance.
• **[Lightweight Spatio-Temporal Attention Network
  Q&A: Latest 15 Papers - May 20, 2025 =====================================

In this article, we will provide a Q&A section to answer some of the most frequently asked questions about the latest 15 papers in the fields of Time Series, Spatio Temporal, Time Series Imputation, Irregular Time Series, Diffusion Model, and Graph Neural Networks.

Q: What is the main contribution of the paper "An Empirical Bayes approach to ARX Estimation"?

A: The main contribution of the paper "An Empirical Bayes approach to ARX Estimation" is the proposal of an empirical Bayes approach to estimate the parameters of an autoregressive (AR) model with exogenous variables (ARX). The approach is based on the idea of using a prior distribution for the parameters and updating it using the data.

Q: What is the significance of the paper "TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents"?

A: The significance of the paper "TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents" is the introduction of a new benchmark for time series machine learning, called TimeSeriesGym. The benchmark provides a scalable and flexible framework for evaluating the performance of time series machine learning models.

Q: What is the main idea of the paper "Level Generation with Quantum Reservoir Computing"?

A: The main idea of the paper "Level Generation with Quantum Reservoir Computing" is the proposal of a new approach to level generation using quantum reservoir computing. The approach is based on the idea of using a quantum reservoir to learn the patterns in the data.

Q: What is the significance of the paper "Performance of Zero-Shot Time Series Foundation Models on Cloud Data"?

A: The significance of the paper "Performance of Zero-Shot Time Series Foundation Models on Cloud Data" is the evaluation of the performance of zero-shot time series foundation models on cloud data. The results show that the models can achieve high accuracy on cloud data.

Q: What is the main contribution of the paper "True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics"?

A: The main contribution of the paper "True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics" is the proposal of a new approach to zero-shot inference of dynamical systems that preserve long-term statistics. The approach is based on the idea of using a prior distribution for the parameters and updating it using the data.

Q: What is the significance of the paper "Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment"?

A: The significance of the paper "Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment" is the proposal of a new approach to enhancing large language models (LLMs) for time series forecasting. The approach is based on the idea of using a structure-guided cross-modal alignment to align the LLMs with the time series data.

Q: What is the main idea of the paper "RIFLES: Resource-effIcient Federated LEarning via Scheduling"?

A: The main idea of the paper "RIFLES: Resource-effIcient Federated LEarning via Scheduling" is the proposal of a new approach to resource-efficient federated learning via scheduling. The approach is based on the idea of using a scheduling algorithm to allocate the resources efficiently.

Q: What is the significance of the paper "panelPomp: Analysis of Panel Data via Partially Observed Markov Processes in R"?

A: The significance of the paper "panelPomp: Analysis of Panel Data via Partially Observed Markov Processes in R" is the proposal of a new approach to analyzing panel data using partially observed Markov processes in R. The approach is based on the idea of using a Markov process to model the panel data.

Q: What is the main contribution of the paper "Toward Relative Positional Encoding in Spiking Transformers"?

A: The main contribution of the paper "Toward Relative Positional Encoding in Spiking Transformers" is the proposal of a new approach to relative positional encoding in spiking transformers. The approach is based on the idea of using a relative positional encoding to improve the performance of the spiking transformers.

Q: What is the significance of the paper "Time series saliency maps: explaining models across multiple domains"?

A: The significance of the paper "Time series saliency maps: explaining models across multiple domains" is the proposal of a new approach to explaining models across multiple domains using time series saliency maps. The approach is based on the idea of using a saliency map to highlight the important features in the data.

Q: What is the main idea of the paper "Unifying concepts in information-theoretic time-series analysis"?

A: The main idea of the paper "Unifying concepts in information-theoretic time-series analysis" is the proposal of a new approach to unifying concepts in information-theoretic time-series analysis. The approach is based on the idea of using a unified framework to analyze the time series data.

Q: What is the significance of the paper "Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data"?

A: The significance of the paper "Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data" is the proposal of a new approach to exploring neural Granger causality with xLSTMs. The approach is based on the idea of using an xLSTM to model the temporal dependencies in the data.

Q: What is the main contribution of the paper "Testing procedures based on maximum likelihood estimation for Marked Hawkes processes"?

A: The main contribution of the paper "Testing procedures based on maximum likelihood estimation for Marked Hawkes processes" is the proposal of a new approach to testing procedures based on maximum likelihood estimation for marked Hawkes processes. The approach is based on the idea of using a maximum likelihood estimation to test the hypotheses.

Q: What is the significance of the paper "TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis"?

A: The significance of the paper "TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis" is the proposal of a new approach to dual space tiny pre-trained models for rapid time-series analysis. The approach is based on the idea of using a dual space to improve the performance of the pre-trained models.

Q: What is the main idea of the paper "Agent Performing Autonomous Stock Trading under Good and Bad Situations"?

A: The main idea of the paper "Agent Performing Autonomous Stock Trading under Good and Bad Situations" is the proposal of a new approach to an agent performing autonomous stock trading under good and bad situations. The approach is based on the idea of using a reinforcement learning algorithm to train the agent.

Q: What is the significance of the paper "Hybrid Voting-Based Task Assignment in Modular Construction Scenarios"?

A: The significance of the paper "Hybrid Voting-Based Task Assignment in Modular Construction Scenarios" is the proposal of a new approach to hybrid voting-based task assignment in modular construction scenarios. The approach is based on the idea of using a voting algorithm to assign the tasks.

Q: What is the main contribution of the paper "Just Dance with $π$! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection"?

A: The main contribution of the paper "Just Dance with $π$! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection" is the proposal of a new approach to just dance with $π$! A poly-modal inductor for weakly-supervised video anomaly detection. The approach is based on the idea of using a poly-modal inductor to detect the anomalies.

Q: What is the significance of the paper "Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation"?

A: The significance of the paper "Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation" is the proposal of a new approach to time-frequency-based attention cache memory model for real-time speech separation. The approach is based on the idea of using a time-frequency-based attention to improve the performance of the speech separation.

Q: What is the main idea of the paper "Scene-Text Grounding for Text-Based Video Question Answering"?

A: The main idea of the paper "Scene-Text Grounding for Text-Based Video Question Answering" is the proposal of a new approach to scene-text grounding for text-based video question answering. The approach is based on the idea of using a scene-text grounding to improve the performance of the question answering.

Q: What is the significance of the paper "EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting"?

A: The significance of the paper "EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting" is the proposal of a new approach to EpiLLM: Unlocking the potential of large language models in epidemic forecasting. The approach is based on the idea of using a large language model to improve the performance of the epidemic forecasting.

Q: What is the main contribution of the paper "Super-Resolution Generative Adversarial Networks based Video Enhancement"?

A: The main contribution of the paper "Super-Resolution Generative Adversarial Networks based Video Enhancement" is the proposal of a new approach to super-resolution generative adversarial networks based video enhancement. The approach is based on the idea of using a super-resolution generative adversarial network to improve the performance of the video enhancement.

**Q: What is the significance of the paper "DYNUS: Uncertainty-aware Trajectory Planner in Dynamic Unknown