Tootfinder

Evidence for Dual Periodic Signatures in PKS 0805-07 from Multi-technique Time Series Analysis
Sikandar Akbar, Zahir Shah, Ranjeev Misra, Sajad Boked, Naseer Iqbal
https://arxiv.org/abs/2506.09602

Evidence for Dual Periodic Signatures in PKS 0805-07 from Multi-technique Time Series Analysis
We report the identification of two statistically significant quasi-periodic oscillations in the weekly binned $γ$-ray light curve of the flat-spectrum radio quasar PKS 0805$-$07, observed by Fermi-LAT over the period MJD 59047.5-59740.5. By applying a suite of complementary time-series analysis techniques, we identify periodic signatures at approximately 255 and 112 days. These techniques include the Lomb-Scargle periodogram (LSP), Weighted Wavelet Z-transform (WWZ), REDFIT, Date-Compensated …

Wasserstein and Convex Gaussian Approximations for Non-stationary Time Series of Diverging Dimensionality
Miaoshiqi Liu, Jun Yang, Zhou Zhou
https://arxiv.org/abs/2506.08723

Wasserstein and Convex Gaussian Approximations for Non-stationary Time Series of Diverging Dimensionality
In high-dimensional time series analysis, Gaussian approximation (GA) schemes under various distance measures or on various collections of subsets of the Euclidean space play a fundamental role in a wide range of statistical inference problems. To date, most GA results for high-dimensional time series are established on hyper-rectangles and their equivalence. In this paper, by considering the 2-Wasserstein distance and the collection of all convex sets, we establish a general GA theory for a br…

Predicting Graph Structure via Adapted Flux Balance Analysis
Sevvandi Kandanaarachchi, Ziqi Xu, Stefan Westerlund, Conrad Sanderson
https://arxiv.org/abs/2507.05806

Predicting Graph Structure via Adapted Flux Balance Analysis
Many dynamic processes such as telecommunication and transport networks can be described through discrete time series of graphs. Modelling the dynamics of such time series enables prediction of graph structure at future time steps, which can be used in applications such as detection of anomalies. Existing approaches for graph prediction have limitations such as assuming that the vertices do not to change between consecutive graphs. To address this, we propose to exploit time series prediction m…

A Novel Hybrid Approach for Time Series Forecasting: Period Estimation and Climate Data Analysis Using Unsupervised Learning and Spline Interpolation
Tanmay Kayal, Abhishek Das, U Saranya
https://arxiv.org/abs/2507.07652

A Novel Hybrid Approach for Time Series Forecasting: Period Estimation and Climate Data Analysis Using Unsupervised Learning and Spline Interpolation
This article explores a novel approach to time series forecasting applied to the context of Chennai's climate data. Our methodology comprises two distinct established time series models, leveraging their strengths in handling seasonality and periods. Notably, a new algorithm is developed to compute the period of the time series using unsupervised machine learning and spline interpolation techniques. Through a meticulous ensembling process that combines these two models, we achieve optimized for…

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition
Shibo Li, Yao Zheng
https://arxiv.org/abs/2506.06919 https://

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition
As tensor-valued data become increasingly common in time series analysis, there is a growing need for flexible and interpretable models that can handle high-dimensional predictors and responses across multiple modes. We propose a unified framework for high-dimensional tensor stochastic regression based on CANDECOMP/PARAFAC (CP) decomposition, which encompasses vector, matrix, and tensor responses and predictors as special cases. Tensor autoregression naturally arises as a special case within th…

Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
Sukru Selim Calik, Andac Akyuz, Zeynep Hilal Kilimci, Kerem Colak
https://arxiv.org/abs/2506.06345

Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 in…

London Blue Light Collaboration Evaluation: A Comparative Analysis of Spatio temporal Patterns on Emergency Services by London Ambulance Service and London Fire Brigade
Fangyuan Li, Yijing Li, Luke Edward Rogerson
https://arxiv.org/abs/2506.06011

London Blue Light Collaboration Evaluation: A Comparative Analysis of Spatio temporal Patterns on Emergency Services by London Ambulance Service and London Fire Brigade
With rising demand for emergency services, the London Ambulance Service, LAS, and the London Fire Brigade, LFB, face growing challenges in resource coordination. This study investigates the temporal and spatial similarities in their service demands to assess potential for routine cross-agency collaboration. Time series analysis revealed aligned demand peaks in summer, on Fridays, during daytime hours, and were highly sensitive to high temperature weather conditions. Bivariate mapping and Moran …

FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis
Wenyan Xu, Dawei Xiang, Yue Liu, Xiyu Wang, Yanxiang Ma, Liang Zhang, Chang Xu, Jiaheng Zhang
https://arxiv.org/abs/2506.05019

FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis
Pure time series forecasting tasks typically focus exclusively on numerical features; however, real-world financial decision-making demands the comparison and analysis of heterogeneous sources of information. Recent advances in deep learning and large scale language models (LLMs) have made significant strides in capturing sentiment and other qualitative signals, thereby enhancing the accuracy of financial time series predictions. Despite these advances, most existing datasets consist solely of …

A cautious user's guide in applying HMMs to physical systems
Max Schweiger, Ayush Saurabh, Steve Press\'e
https://arxiv.org/abs/2506.05707 https://…

A cautious user's guide in applying HMMs to physical systems
Nature, as far as we know, evolves continuously through space and time. Yet the ubiquitous hidden Markov model (HMM)--originally developed for discrete time and space analysis in natural language processing--remains a central tool in interpreting time series data drawn from from physical systems. This raises a fundamental question: What are the implications of applying a discrete-state, discrete-time framework to analyze data generated by a continuously evolving system? Through synthetic data g…

Delving into the depths of NGC 3783 with XRISM. I. Kinematic and ionization structure of the highly ionized outflows
Missagh Mehdipour, Jelle S. Kaastra, Megan E. Eckart, Liyi Gu, Ralf Ballhausen, Ehud Behar, Camille M. Diez, Keigo Fukumura, Matteo Guainazzi, Kouichi Hagino, Timothy R. Kallman, Erin Kara, Chen Li, Jon M. Miller, Misaki Mizumoto, Hirofumi Noda, Shoji Ogawa, Christos Panagiotou, Atsushi Tanimoto, Keqin Zhao

Delving into the depths of NGC 3783 with XRISM. I. Kinematic and ionization structure of the highly ionized outflows
We present our study of the XRISM observation of the Seyfert-1 galaxy NGC 3783. For the first time, XRISM's Resolve microcalorimeter enables a detailed characterization of the highly ionized outflows in this active galactic nucleus. Our analysis constrains their outflow and turbulent velocities, along with their ionization parameter $ξ$ and column density $N_{\rm H}$. The high-resolution Resolve spectrum reveals a distinct series of Fe absorption lines between 6.4 and 7.8 keV, ranging from Fe …

Symbolic Higher-Order Analysis of Multivariate Time Series
Andrea Civilini, Fabrizio de Vico Fallani, Vito Latora
https://arxiv.org/abs/2506.00508 https://…

Symbolic Higher-Order Analysis of Multivariate Time Series
Identifying patterns of relations among the units of a complex system from measurements of their activities in time is a fundamental problem with many practical applications. Here, we introduce a method that detects dependencies of any order in multivariate time series data. The method first transforms a multivariate time series into a symbolic sequence, and then extract statistically significant strings of symbols through a Bayesian approach. Such motifs are finally modelled as the hyperedges …

Uniform Reanalysis of JWST MIRI 15{\mu}m Exoplanet Eclipse Observations using Frame-Normalized Principal Component Analysis
Nicholas J. Connors, Christopher Monaghan, Bj\"orn Benneke, Lisa Dang
https://arxiv.org/abs/2507.02052

Uniform Reanalysis of JWST MIRI 15μm Exoplanet Eclipse Observations using Frame-Normalized Principal Component Analysis
JWST MIRI 15 micron time-series eclipse photometry presents a powerful way to probe for the presence of atmospheres on low-temperature rocky exoplanets orbiting nearby stars. Here, we introduce a novel technique, frame-normalized principal component analysis (FN-PCA) to analyze and detrend these MIRI time-series observations. Using the FN-PCA technique, we perform a uniform reanalysis of the published MIRI 15 micron observations of LHS 1478 b, TOI-1468 b, LHS 1140 c, TRAPPIST-1 b, and TRAPPIST-…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Pola Bereta, Ioannis Diamantis
https://arxiv.org/abs/2506.19759 https://…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Understanding temporal patterns in online search behavior is crucial for real-time marketing and trend forecasting. Google Trends offers a rich proxy for public interest, yet the high dimensionality and noise of its time-series data present challenges for effective clustering. This study evaluates three unsupervised clustering approaches, Symbolic Aggregate approXimation (SAX), enhanced SAX (eSAX), and Topological Data Analysis (TDA), applied to 20 Google Trends keywords representing major cons…

This https://arxiv.org/abs/2505.18570 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis
Stock price prediction remains a complex and high-stakes task in financial analysis, traditionally addressed using statistical models or, more recently, language models. In this work, we introduce VISTA (Vision-Language Inference for Stock Time-series Analysis), a novel, training-free framework that leverages Vision-Language Models (VLMs) for multi-modal stock forecasting. VISTA prompts a VLM with both textual representations of historical stock prices and their corresponding line charts to pre…

A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
Martin Beseda, Vittorio Cortellessa, Daniele Di Pompeo, Luca Traini, Michele Tucci
https://arxiv.org/abs/2506.04204

A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
This paper addresses the challenge of accurately detecting the transition from the warmup phase to the steady state in performance metric time series, which is a critical step for effective benchmarking. The goal is to introduce a method that avoids premature or delayed detection, which can lead to inaccurate or inefficient performance analysis. The proposed approach adapts techniques from the chemical reactors domain, detecting steady states online through the combination of kernel-based step …

Fluctuations in the number of local minima in discrete-time fractional Brownian motion
Maxim Dolgushev, Olivier B\'enichou
https://arxiv.org/abs/2506.04159

Fluctuations in the number of local minima in discrete-time fractional Brownian motion
The analysis of local minima in time series data and random landscapes is essential across numerous scientific disciplines, offering critical insights into system dynamics. Recently, Kundu, Majumdar, and Schehr derived the exact distribution of the number of local minima for a broad class of Markovian symmetric walks [Phys. Rev. E \textbf{110}, 024137 (2024)]; however, many real-world systems are non-Markovian, typically due to interactions with possibly hidden degrees of freedom. This work inv…

Optimizing Solar Energy Production in the USA: Time-Series Analysis Using AI for Smart Energy Management
Istiaq Ahmed, Md Asif Ul Hoq Khan, MD Zahedul Islam, Md Sakibul Hasan, Tanaya Jakir, Arat Hossain, Joynal Abed, Muhammad Hasanuzzaman, Sadia Sharmeen Shatyi, Kazi Nehal Hasnain
https://arxiv.org/abs/2506.23368

Optimizing Solar Energy Production in the USA: Time-Series Analysis Using AI for Smart Energy Management
As the US rapidly moves towards cleaner energy sources, solar energy is fast becoming the pillar of its renewable energy mix. Even while solar energy is increasingly being used, its variability is a key hindrance to grid stability, storage efficiency, and system stability overall. Solar energy has emerged as one of the fastest-growing renewable energy sources in the United States, adding noticeably to the country's energy mix. Retrospectively, the necessity of inserting the sun's energy into th…

Predicting Thermodynamics of Liquid Water from Time Series Analysis
Ma{\l}gorzata J. Zimo\'n, Fausto Martelli
https://arxiv.org/abs/2506.21821 https://…

Predicting Thermodynamics of Liquid Water from Time Series Analysis
Thermodynamics, introduced over two centuries ago, remains foundational to our understanding of physical, chemical, biological, and engineering systems. Its principles are traditionally grounded in the statistical mechanics framework, which explains macroscopic behavior from microscopic states. In this work, we propose an alternative approach that interprets thermodynamic behavior through the lenses of time series analysis, an approach commonly used in other fields, including finance, climate, …

This https://arxiv.org/abs/2410.21527 has been replaced.
link: https://scholar.google.com/scholar?q=a

VISTA-SSM: Varying and Irregular Sampling Time-series Analysis via State Space Models
We introduce VISTA, a clustering approach for multivariate and irregularly sampled time series based on a parametric state space mixture model. VISTA is specifically designed for the unsupervised identification of groups in datasets originating from healthcare and psychology where such sampling issues are commonplace. Our approach adapts linear Gaussian state space models (LGSSMs) to provide a flexible parametric framework for fitting a wide range of time series dynamics. The clustering approac…

Analysis of Multiple Long Run Relations in Panel Data Models with Applications to Financial Ratios
Alexander Chudik, M. Hashem Pesaran, Ron P. Smith
https://arxiv.org/abs/2506.02135

Analysis of Multiple Long Run Relations in Panel Data Models with Applications to Financial Ratios
This paper provides a new methodology for the analysis of multiple long run relations in panel data models where the cross section dimension, $n$, is large relative to the time series dimension, $T$. For panel data models with large $n$ researchers have focused on panels with a single long run relationship. The main difficulty has been to eliminate short run dynamics without generating significant uncertainty for identification of the long run. We overcome this problem by using non-overlapping …

Distributed lag non-linear models with Laplacian-P-splines for analysis of spatially structured time series
Sara Rutten, Bryan Sumalinab, Oswaldo Gressani, Thomas Neyens, Elisa Duarte, Niel Hens, Christel Faes
https://arxiv.org/abs/2506.04814

Distributed lag non-linear models with Laplacian-P-splines for analysis of spatially structured time series
Distributed lag non-linear models (DLNM) have gained popularity for modeling nonlinear lagged relationships between exposures and outcomes. When applied to spatially referenced data, these models must account for spatial dependence, a challenge that has yet to be thoroughly explored within the penalized DLNM framework. This gap is mainly due to the complex model structure and high computational demands, particularly when dealing with large spatio-temporal datasets. To address this, we propose a…

DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification
Boris Kriuk, Logic Ng, Zarif Al Hossain
https://arxiv.org/abs/2507.01971

DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification
Support and resistance (SR) levels are central to technical analysis, guiding traders in entry, exit, and risk management. Despite widespread use, traditional SR identification methods often fail to adapt to the complexities of modern, volatile markets. Recent research has introduced machine learning techniques to address the following challenges, yet most focus on price prediction rather than structural level identification. This paper presents DeepSupp, a new deep learning approach for detect…

A three-dimensional, multi-wavelength view and time-dependent analysis of the Milky Way's local ionized gas
Lewis McCallum, Kenneth Wood, Robert Benjamin, Dhanesh Krishnarao, Anna F. McLeod
https://arxiv.org/abs/2506.17689

A three-dimensional, multi-wavelength view and time-dependent analysis of the Milky Way's local ionized gas
This work is the continuation of a series attempting to characterize the local warm ionized medium through both static and time dependent simulations. We build upon our three dimensional, observationally-derived simulation of the local photoionized interstellar medium - based on static photoionization simulations constrained by 3D dust maps - to include metals required to predict collisionally excited optical and infrared emission lines, providing the first all-sky prediction of a series of lin…

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Thomas King, Sergio Vinciguerra
https://arxiv.org/abs/2505.21312 https://

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Understanding how faults nucleate and grow is a critical problem in earthquake science and hazard assessment. This study examines fault development in Alzo granite under triaxial pressures ranging from 5 to 40 MPa by applying a Time Delay Neural Network (TDNN) to multi-parameter acoustic emission (AE) data. The TDNN integrates waveform-derived attributes, including peak delay and scattering attenuation, with occurrence-based metrics such as time distributions, Gutenberg-Richter b-values, and sp…

This https://arxiv.org/abs/2505.17662 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs
Transformer-based models have shown strong performance across diverse time-series tasks, but their deployment on resource-constrained devices remains challenging due to high memory and computational demand. While prior work targeting Microcontroller Units (MCUs) has explored hardware-specific optimizations, such approaches are often task-specific and limited to 8-bit fixed-point precision. Field-Programmable Gate Arrays (FPGAs) offer greater flexibility, enabling fine-grained control over data …

Fourier minimization and imputation of time series
Will Burstein, Alex Iosevich, Azita Mayeli, Hari Sarang Nathan
https://arxiv.org/abs/2506.19226 https://…

Fourier minimization and imputation of time series
One of the most common procedures in modern data analytics is filling in missing values in times series. For a variety of reasons, the data provided by clients to obtain a forecast, or other forms of data analysis, may have missing values, and those values need to be filled in before the data set can be properly analyzed. Many freely available forecasting software packages, such as the sktime library, have built-in mechanisms for filling in missing values. The purpose of this paper is to adapt …

A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features
Korkut Anapa, \.Ismail G\"uzel, Ceylan Yozgatl{\i}gil
https://arxiv.org/abs/2507.02890

A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features
Purpose: Overall equipment efficiency (OEE) is a key manufacturing KPI, but its volatile nature complicates short-term forecasting. This study presents a novel framework combining time series decomposition and topological data analysis to improve OEE prediction across various equipment, such as hydraulic press systems. Methods: The approach begins by decomposing hourly OEE data into trend, seasonal, and residual components. The residual, capturing short-term variability, is modeled using a se…

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
Dominik Stempie\'n, Robert \'Slepaczuk
https://arxiv.org/abs/2505.19617

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comp…

Deep learning for classifying dynamical states from time series via recurrence plots
Athul Mohan, G. Ambika, Chandrakala Meena
https://arxiv.org/abs/2506.17498

Deep learning for classifying dynamical states from time series via recurrence plots
Recurrence Quantification Analysis (RQA) is a widely used method for capturing the dynamical structure embedded in time series data, relying on the analysis of recurrence patterns in the reconstructed phase space via recurrence plots. Although RQA proves effective across a range of applications, it typically requires the computation of multiple quantitative measures, making it both computationally intensive and sensitive to parameter choices. In this study, we adopt an alternative approach that…

Video-based Direct Time Series Measurement of Along-Strike Slip on the Coseismic Surface Rupture During the 2025 Mw7.7 Myanmar Earthquake
Jianhao Gao, Fuhua Zheng, Chaofeng Wang, Haoran Meng
https://arxiv.org/abs/2505.20494

Video-based Direct Time Series Measurement of Along-Strike Slip on the Coseismic Surface Rupture During the 2025 Mw7.7 Myanmar Earthquake
This study presents a time-resolved analysis of coseismic lateral surface rupture along the Sagaing Fault during the Mw 7.7 Mandalay, Myanmar earthquake on March 28, 2025. Leveraging a publicly available Closed-Circuit Television (CCTV) footage alongside on-site measurements, we show the first in-situ high sampling rate direct measurement of a coseismic slip evolution of a fault during an earthquake. Our work comprises four primary stages: data acquisition, video pre-processing, object tracking…

This https://arxiv.org/abs/2506.04508 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Mechanistic models for panel data: Analysis of ecological experiments with four interacting species
In an ecological context, panel data arise when time series measurements are made on a collection of ecological processes. Each process may correspond to a spatial location for field data, or to an experimental ecosystem in a designed experiment. Statistical models for ecological panel data should capture the high levels of nonlinearity, stochasticity, and measurement uncertainty inherent in ecological systems. Furthermore, the system dynamics may depend on unobservable variables. This study ap…

SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis
Zhixin Guo, Qi Shi, Xiaofan Xu, Sixiang Shan, Limin Qin, Linqiang Ge, Rui Zhang, Ya Dai, Hua Zhu, Guowei Jiang
https://arxiv.org/abs/2506.13034

SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis
With the rapid advancement of aerospace technology and the large-scale deployment of low Earth orbit (LEO) satellite constellations, the challenges facing astronomical observations and deep space exploration have become increasingly pronounced. As a result, the demand for high-precision orbital data on space objects-along with comprehensive analyses of satellite positioning, constellation configurations, and deep space satellite dynamics-has grown more urgent. However, there remains a notable l…

Intraday Functional PCA Forecasting of Cryptocurrency Returns
Joann Jasiak, Cheng Zhong
https://arxiv.org/abs/2505.20508 https://arxi…

Intraday Functional PCA Forecasting of Cryptocurrency Returns
We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functional and discrete time dynamic models. It offers a convenient framework for functional time series analysis. For intraday forecasting, we intr…

Universal Modelling of Autocovariance Functions via Spline Kernels
Lachlan Astfalck
https://arxiv.org/abs/2506.21953 https://arxiv.or…

Universal Modelling of Autocovariance Functions via Spline Kernels
Flexible modelling of the autocovariance function (ACF) is central to time-series, spatial, and spatio-temporal analysis. Modern applications often demand flexibility beyond classical parametric models, motivating non-parametric descriptions of the ACF. Bochner's Theorem guarantees that any positive spectral measure yields a valid ACF via the inverse Fourier transform; however, existing non-parametric approaches in the spectral domain rarely return closed-form expressions for the ACF itself. We…

Characterizing asymmetric and bimodal long-term financial return distributions through quantum walks
Stijn De Backer, Luis E. C. Rocha, Jan Ryckebusch, Koen Schoors
https://arxiv.org/abs/2505.13019

Characterizing asymmetric and bimodal long-term financial return distributions through quantum walks
The analysis of logarithmic return distributions defined over large time scales is crucial for understanding the long-term dynamics of asset price movements. For large time scales of the order of two trading years, the anticipated Gaussian behavior of the returns often does not emerge, and their distributions often exhibit a high level of asymmetry and bimodality. These features are inadequately captured by the majority of classical models to address financial time series and return distributio…

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Harrison Katz, Liz Medina, Robert E. Weiss
https://arxiv.org/abs/2506.13973

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Prior choice can strongly influence Bayesian Dirichlet ARMA (B-DARMA) inference for compositional time-series. Using simulations with (i) correct lag order, (ii) overfitting, and (iii) underfitting, we assess five priors: weakly-informative, horseshoe, Laplace, mixture-of-normals, and hierarchical. With the true lag order, all priors achieve comparable RMSE, though horseshoe and hierarchical slightly reduce bias. Under overfitting, aggressive shrinkage-especially the horseshoe-suppresses noise …

Mechanistic models for panel data: Analysis of ecological experiments with four interacting species
Bo Yang, Jesse Wheeler, Meghan A. Duffy, Aaron A. King, Edward L. Ionides
https://arxiv.org/abs/2506.04508

Mechanistic models for panel data: Analysis of ecological experiments with four interacting species
In an ecological context, panel data arise when time series measurements are made on a collection of ecological processes. Each process may correspond to a spatial location for field data, or to an experimental ecosystem in a designed experiment. Statistical models for ecological panel data should capture the high levels of nonlinearity, stochasticity, and measurement uncertainty inherent in ecological systems. Furthermore, the system dynamics may depend on unobservable variables. This study ap…

Hierarchical Representations for Evolving Acyclic Vector Autoregressions (HEAVe)
Cameron Cornell, Lewis Mitchell, Matthew Roughan
https://arxiv.org/abs/2505.12806

Hierarchical Representations for Evolving Acyclic Vector Autoregressions (HEAVe)
Causal networks offer an intuitive framework to understand influence structures within time series systems. However, the presence of cycles can obscure dynamic relationships and hinder hierarchical analysis. These networks are typically identified through multivariate predictive modelling, but enforcing acyclic constraints significantly increases computational and analytical complexity. Despite recent advances, there remains a lack of simple, flexible approaches that are easily tailorable to sp…