Time Series Data Research Articles

Determinants of the Developed Country Index and Indonesian Macroeconomic on the IDX Growth 30 and IDX Value 30

This study analyzes the determinants of the Developed Country Index (Hang Seng Index, Dow Jones Index) and Indonesian macroeconomic variables (10-Year Government Bond Yield, Foreign Ownership in Government Bonds, and the BI-7 Day Reverse Repo Rate, BI7DRR) on the movement of the IDX Growth 30 Index (IDXG30) and IDX Value 30 Index (IDXV30) from 2018–2022. Monthly time series data is used with a saturated sampling technique, processed via Eviews 12 using VECM analysis. The study conducts Stationarity, Optimal Lag, VAR Stability, and Cointegration Tests, along with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses Results show that the Hang Seng Index and Foreign Ownership in Government Bonds negatively affect IDXG30 and IDXV30, while BI7DRR positively influences both. The Dow Jones Index positively affects IDXG30 but negatively impacts IDXV30. Meanwhile, the 10-Year Government Bond Yield negatively affects IDXG30 and positively influences IDXV30. Additionally, the performance of IDXG30 and IDXV30 themselves and BI7DRR significantly contribute to both indices' movements.

Soft sensor for melting flow rate prediction based on data-enhanced classification method

Abstract The melt flow rate (MFR) is a crucial quality indicator for polyolefin products. However, the offline laboratory measurement method suffers from significant time delays between sampling and obtaining test values, making it inconvenient for real-time product quality control. Additionally, installing an online analyzer can be expensive. In this study, we propose a soft sensor method that combines mechanism analysis and data-driven approaches to predict the MFR in the extruder process. Firstly, we analyze the physical mechanism of the extruder process, and the time series data are processed into independent enhanced data sets by reasonably selecting data enhancement parameters. Secondly, we statistically model the distribution of particle MFR and classify the data into four sections. We train Deep Neural Network (DNN) and Gradient Boosting Decision Tree (GBDT) regression models separately for low and non-low MFR sections while also developing a global classification model to invoke corresponding regression models when needed. The results demonstrate that our classification model achieves 100% accuracy in distinguishing between low and non-low MFR categories; furthermore, our regression models achieve an RMSE of 0.600, and an R2 value of 0.998, outperforming existing models on various indicators. Finally, considering viscosity variations in polyolefin products' influence on prediction performance, we recommend setting data enhancement parameters contrary to particle MFR trends.

Diff-MTS: Temporal-Augmented Conditional Diffusion-Based AIGC for Industrial Time Series Toward the Large Model Era.

Industrial multivariate time series (MTS) is a critical view of the industrial field for people to understand the state of machines. However, due to data collection difficulty and privacy concerns, available data for building industrial intelligence and industrial large models is far from sufficient. Therefore, industrial time series data generation is of great importance. Existing research usually applies generative adversarial networks (GANs) to generate MTS. However, GANs suffer from the unstable training process due to the joint training of the generator and discriminator. This article proposes a temporal-augmented conditional adaptive diffusion model, termed Diff-MTS, for MTS generation. It aims to better handle the complex temporal dependencies and dynamics of MTS data. Specifically, a conditional adaptive maximum-mean discrepancy (Ada-MMD) method has been proposed for the controlled generation of MTS, which does not require a classifier to control the generation. It improves the condition consistency of the diffusion model. Moreover, a temporal decomposition reconstruction UNet (TDR-UNet) is established to capture complex temporal patterns and further improve the quality of the synthetic time series. Comprehensive experiments on the C-MAPSS and FEMTO datasets demonstrate that the proposed Diff-MTS performs substantially better in terms of diversity, fidelity, and utility compared with the GAN-based methods. These results show that Diff-MTS facilitates the generation of industrial data, contributing to intelligent maintenance and the construction of industrial large models.

Explainability of CNN-based Alzheimer's disease detection from online handwriting.

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer's disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack of explainability in deep learning models for online handwriting analysis is a critical gap in the literature in the context of Alzheimer's disease detection. This paper addresses this challenge by interpreting predictions from a Convolutional Neural Network applied to multivariate time series data, generated by online handwriting data associated with continuous loop series handwritten on a graphical tablet. Our explainability methods reveal distinct motor behavior characteristics for healthy individuals and those diagnosed with Alzheimer's. Healthy subjects exhibited consistent, smooth movements, while Alzheimer's patients demonstrated erratic patterns marked by abrupt stops and direction changes. This emphasizes the critical role of explainability in translating complex models into clinically relevant insights. Our research contributes to the enhancement of early diagnosis, providing significant and reliable insights to stakeholders involved in patient care and intervention strategies. Our work bridges the gap between machine learning predictions and clinical insights, fostering a more effective and understandable application of advanced models for Alzheimer's disease assessment.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BackgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.ResultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism’s specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.ConclusionsWhile we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Dual channel visible graph convolutional neural network for microleakage monitoring of pipeline weld homalographic cracks

When using a single sensor to monitor early microleakage of nuclear power pressure pipeline leakage, there are problems such as low monitoring accuracy and poor early warning reliability due to the limitations of the monitoring range and weak difference between the leakage signals. To address these challenges, this paper proposes a dual channel visible graph convolutional neural network (DCV-GCN). Firstly, the acoustic emission time-series data of each channel are truncated and divided, and the significant frequency bands are selected based on the envelope spectrum. On this basis, the sequence group is averaged to obtain the graph structure sequence. Then, the limited penetrable visibility (LPV) graph construction algorithm is used to calculate the adjacency matrix, and the important nodes is reserved according to the eigenvector centrality. Furthermore, the inverse ratio of the distance from the sensor in each single channel to the center of the crack is used as the fusion weight, and the adjacency matrices are merged after normalization to transform the construction of the graph structure dataset. Finally, the dataset is input into the graph convolutional neural network, and the effectiveness of the method is verified by carefully designing three homalographic cracks. The results show that the proposed method can effectively extract the distinguishing features with similar frequency components and similar leakage rates, and the recognition accuracy of different leakage states can reach 98.56 %. In addition, through ablation experiments and different parameter strategy settings, the operating mechanism is explained, which can provide a reference for monitoring and analysis by industrial technicians.

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

ABSTRACT Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

Time series forecasting using transformer neural network

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is proposed.This model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes.

Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.

In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types, including medical images (eg, MRI and CT scans), time-series data (eg, sensor data from wearable devices and electronic health records), audio recordings (eg, heart and respiratory sounds and patient interviews), text (eg, clinical notes and research articles), videos (eg, surgical procedures), and omics data (eg, genomics and proteomics). While advancements in large language models (LLMs) have enabled new applications for knowledge retrieval and processing in the medical field, most LLMs remain limited to processing unimodal data, typically text-based content, and often overlook the importance of integrating the diverse data modalities encountered in clinical practice. This paper aims to present a detailed, practical, and solution-oriented perspective on the use of multimodal LLMs (M-LLMs) in the medical field. Our investigation spanned M-LLM foundational principles, current and potential applications, technical and ethical challenges, and future research directions. By connecting these elements, we aimed to provide a comprehensive framework that links diverse aspects of M-LLMs, offering a unified vision for their future in health care. This approach aims to guide both future research and practical implementations of M-LLMs in health care, positioning them as a paradigm shift toward integrated, multimodal data-driven medical practice. We anticipate that this work will spark further discussion and inspire the development of innovative approaches in the next generation of medical M-LLM systems.

Periodformer: An efficient long-term time series forecasting method based on periodic attention

As Transformer-based models have achieved impressive performance across various time series tasks, Long-Term Series Forecasting (LTSF) has garnered extensive attention in recent years. The intricate complexity of the Attention mechanism leads to pronounced overfitting of time series data, thereby impeding the efficient and accurate forecasting of long-term sequences. To better cope with this, we design a lightweight Period-Attention mechanism (Periodformer), which renovates the aggregation of long-term subseries via explicit periodicity and short-term subseries via built-in proximity. Meanwhile, a gating mechanism is embedded into Periodformer to regulate the influence of the attention module on the prediction results. This gives Periodformer a much more powerful and flexible sequence modeling capability with linear computational complexity, guaranteeing higher prediction performance and shorter runtime on real devices. Experimental results show that Periodformer consistently performs best on six widely used benchmark datasets. Compared with the state-of-the-art methods, the prediction error of Periodformer was reduced by 13% and 26% for multivariate and univariate forecasting, respectively.

Wave-Former: Lag removing univariate long time series forecasting transformer for ocean waves

Long-Sequence Time Series Forecasting (LSTSF) of ocean waves using univariate modelling has always been a challenging task in the field of oceanography as it demands a high prediction capacity model capable to capture precise long-range dependency between input and output variables. Inherently, it exhibits the problem of “time lag or phase shift” in predictions which is due to high autocorrelation in between time dependent sequential values. Very few researchers had tried to remove this “phase shift” problem in the univariate modelling including the author of present work wherein preprocessing of time series data was done by different methods. Many failed in gaining high prediction accuracy at longer forecasting intervals. Overcoming these limitations, present work aims for LSTSF of ocean waves ranging from 24 to 144 hours (hrs) at the three locations of Gulf of Mexico using “WaveFormer” architecture based on the Informer. Probabilistic self-attention mechanism has proved to be efficient to remove the “time lag or phase shift” in ocean waves as well as in predicting the waves at large time intervals (144 hrs). Low Root Mean Squared Errors (RMSEs) (0.045–0.100) and high correlation coefficients (0.90–0.99), low values of test loss (0.002–0.012) of all the developed models demonstrated the competency of WaveFormer.

Reduced physiology-induced temporal instability achieved with variable-flip-angle fast low-angle excitation echo-planar technique with multishot echo planar time-resolved imaging.

Echo planar time-resolved imaging (EPTI) is a new imaging approach that addresses the limitations of EPI by providing high-resolution, distortion- and T2/ blurring-free imaging for functional MRI (fMRI). However, as in all multishot sequences, intershot phase variations induced by physiological processes can introduce temporal instabilities to the reconstructed time-series data. This study aims to reduce these instabilities in multishot EPTI. In conventional multishot EPTI, the time intervals between the shots comprising each slice can introduce intershot phase variations. Here, the fast low-angle excitation echo-planar technique (FLEET), in which all shots of each slice are acquired consecutively with minimal time delays, was combined with a variable flip angle (VFA) technique to improve intershot consistency and maximize signal. A recursive Shinnar-Le Roux RF pulse design algorithm was used to generate pulses for different shots to produce consistent slice profiles and signal intensities across shots. Blipped controlled aliasing in parallel imaging simultaneous multislice was also combined with the proposed VFA-FLEET EPTI to improve temporal resolution and increase spatial coverage. The temporal stability of VFA-FLEET EPTI was compared with conventional EPTI at 7 T. The results demonstrated that VFA-FLEET can provide spatial-specific increase of temporal stability. We performed high-resolution task-fMRI experiments at 7 T using VFA-FLEET EPTI, and reliable BOLD responses to a visual stimulus were detected. The intershot phase variations induced by physiological processes in multishot EPTI can manifest as specific spatial patterns of physiological noise enhancement and lead to reduced temporal stability. The VFA-FLEET technique can substantially reduce these physiology-induced instabilities in multishot EPTI acquisitions. The proposed method provides sufficient stability and sensitivity for high-resolution fMRI studies.

Vessel Trajectory Prediction Based on Automatic Identification System Data: Multi-Gated Attention Encoder Decoder Network

Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction.

TimeNorm: a novel normalization method for time course microbiome data.

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis.

Forecasting intraoperative hypotension during hepatobiliary surgery.

Prediction and avoidance of intraoperative hypotension (IOH) can lead to less postoperative morbidity. Machine learning (ML) is increasingly being applied to predict IOH. We hypothesize that incorporating demographic and physiological features in an ML model will improve the performance of IOH prediction. In addition, we added a "dial" feature to alter prediction performance. An ML prediction model was built based on a multivariate random forest (RF) trained algorithm using 13 physiologic time series and patient demographic data (age, sex, and BMI) for adult patients undergoing hepatobiliary surgery. A novel implementation was developed with an adjustable, multi-model voting (MMV) approach to improve performance in the challenging context of a dynamic, sliding window for which the propensity of data is normal (negative for IOH). The study cohort included 85% of subjects exhibiting at least one IOH event. Males constituted 70% of the cohort, median age was 55.8years, and median BMI was 27.7. The multivariate model yielded average AUC = 0.97 in the static context of a single prediction made up to 8min before a possible IOH event, and it outperformed a univariate model based on MAP-only (average AUC = 0.83). The MMV model demonstrated AUC = 0.96, PPV = 0.89, and NPV = 0.98 within the challenging context of a dynamic sliding window across 40min prior to a possible IOH event. We present a novel ML model to predict IOH with a distinctive "dial" on sensitivity and specificity to predict first IOH episode during liver resection surgeries.

A data- and knowledge-driven framework for developing machine learning models to predict soccer match outcomes

AbstractThe 2023 Soccer Prediction Challenge invited the machine learning community to develop innovative methods to predict the outcomes of 736 future soccer matches. The Challenge included two tasks. Task 1 was to forecast the exact match score, i.e., the number of goals scored by each team. Task 2 was to predict the match outcome as probability vector over the three possible result categories: victory of the home team, draw, and victory of the away team. Here, we present a new data- and knowledge-driven framework for building machine learning models from readily available data to predict soccer match outcomes. A key component of this framework is an innovative approach to modeling interdependent time series data of competing entities. Using this framework, we developed various predictive models based on k-nearest neighbors, artificial neural networks, naive Bayes, and ordinal forests, which we applied to the two tasks of the 2023 Soccer Prediction Challenge. Among all submissions to the Challenge, our machine learning models based on k-nearest neighbors and neural networks achieved top performances. Our main insights from the Challenge are that relatively simple learning algorithms perform remarkably well compared to more complex algorithms, and that the key to successful predictions lies in how well soccer domain knowledge can be incorporated in the modeling process.

Modeling the two-dimensional variations in EEG signals to analyze the impact of music on sleep states

Variations in music intensity and pitch can significantly impact sleep and alleviate insomnia through relaxing music. Traditional EEG analysis methods struggle with capturing temporal features and managing time-series data complexity. To address these challenges, we propose an improved Temporal Network model integrated with a Temporal Self-Attention (TSA) mechanism. This model enhances time-step feature capture in EEG signals, transforming one-dimensional EEG data into two-dimensional tensors to capture multi-scale temporal features. Experiments on the DEAP dataset demonstrate significant performance improvements, with 84.26% accuracy, 90.06% precision, and 97.82% recall, outperforming current models. Our model effectively analyzes the impact of relaxing music on different sleep stages, providing insights for improving sleep quality. Future work will optimize the model structure, incorporate additional deep learning techniques, and expand the scope to include multiple physiological signals, aiming to enhance the clinical value for sleep disorder diagnosis and personalized music therapy.

Modeling and Forecasting High Frequency Currency Exchange Rates. A Comparative Study of ARIMA, ANN, and Hybrid ARIMA-ANN Models

Aims/ Objectives: The study develops comparative results on the modeling and prediction performance of the autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and the hybrid ARIMA-ANN time series models for high frequency data. Methodology: The study made use of ARIMA, ANN, and hybrid ARIMA-ANN models to forecasts the East Africa Community countries' daily currency exchange rates data which were obtained from the Central Bank of Kenya website and covered the period from January 2017 to December 2023. Stationarity of the time series data was established using the ADF test. The Ljung Box test and ACF plots were used to establish and compare the goodness-of-fit of the resultant models while the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error(RMSE) values were used to compare the prediction performance. Results: The study established that the hybrid ARIMA-ANN methodolgy provided better-fitting models for the currency exchange rates data compared to ARIMA and ANN modeling strategies since all the Lyung Box test statistics had p values greater than 5%. Comparatively, the hybrid methodology registered lower MAPE and RMSE values hence had better prediction accuracy compared to ARIMA and ANN methods. Conclusion: The Hybid methodology improves the modeling and forecasting accuracy over the ARIMA and ANN models for high frequency time series data due to its ability to captures both the linear and nonlinear patterns in the time series data.

DIGITAL PAYMENTS AND FRAUD CONNECTION: INSIGHTS FROM THE INDIAN ECONOMY

In the dynamic world of digital transactions in India, the symbiotic relationship between convenience and vulnerability has become increasingly apparent. This study aims to know how digital payment and fraud connect in Indian economy. The primary objective of this study is to know the relationship between financial fraud with digital payment infrastructures, value of digital payments, and volume of digital payment. The data is collected through RBI’s official quarterly reports from September 2022 to May 2023. The collected time series data for all the variables has been analyzed for descriptive statistics. These series were tested for stationarity using Augmented Dickey–Fuller test (ADF - unit root test), Correlogram, and time series chart. The correlation and regression analysis has been performed on this time series data using SPSS and EViews software. From the correlation results this study reveals that there is no significant correlation between value and volume of fraud with payment infrastructure, value of digital payment, and volume of digital payment. The value of UPI transactions has a significant negative impact on digital payment fraud, indicating a potential protective effect.

From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary

Biomechanical data collection was largely confined to controlled laboratory setups, relying on marker-based systems or force platforms. However, the emergence of wearable sensors and markerless motion capture has revolutionized this field, enabling data collection in real-world scenarios. This shift has also sparked interest in integrating machine learning (ML) into biomechanical workflows, promising to revolutionize data acquisition in field settings and refine data analysis (Halilaj et al., 2018). Figure 1 provides an overview of corresponding ML applications for key tasks in the biomechanical workflow. This commentary explores the transformative potential of ML in biomechanics, focusing on enhancing data collection and analysis in real-world environments. Pose estimation (a) is the process of automatically tracking and determining the body’s anatomical landmarks, body segments, or joint centre locations in video images using ML, enabling the quantification of human movement without marker and sensor attachments to the human body. Feature estimation (b) employs ML to predict complex biomechanical data, e.g. joint moments from more accessible data sources, including IMUs, pressure insoles, and RGB video cameras. Event detection (c) in time series data is the annotation of certain events that are used to extract useful and vital information or to remove unwanted and unnecessary data for further analysis. Clustering (d) involves grouping instances or individuals with similar biomechanical characteristics using unsupervised ML, thereby revealing underlying structures and subgroups within complex biomechanical data. Finally, automated classification (e) refers to the process of developing a predictive model that assigns input features of data samples to predefined categories or classes using supervised ML. Despite the advancements of ML in biomechanics, central challenges are faced. Estimation errors remain critically high depending on the task and application field, necessitating a careful reflection on data acquisition potentials. Furthermore, especially complex Deep Learning models, while showing promising performances, exhibit a lack of transparency in understanding their decision-making processes and the underlying patterns and rules learned from the data. This phenomenon, often termed as the black-box nature of these models, poses a considerable obstacle. In response, Explainable Artificial Intelligence (XAI), a field that encompasses different explainability approaches to shed light on the inner workings of complex, non-linear ML models, has gained increasing attention in recent years (Slijepcevic et al., 2023). A further central challenge is the availability of data and annotations, which describes the limited availability or insufficiency of relevant and comprehensive datasets, as well as task-specific annotations, for conducting thorough analyses and research in biomechanics. The lack of large-scale benchmark datasets available restricts the widespread adoption of ML-based approaches in biomechanics. Privacy concerns present significant ethical and legal issues, e.g. with identifiable video data. Additionally, model validation remains a critical problem, as many studies fail to validate their models on diverse datasets, often relying on limited data from a single laboratory. Furthermore, there is a risk that the fundamental mechanical understanding of biomechanical processes might be overshadowed by an over-reliance on ML techniques. In conclusion, the integration of ML into biomechanics presents a transformative opportunity for understanding human movement by enabling and improving data collection and analysis in real-world settings. Challenges in data accessibility and methodological transparency necessitate collaborative efforts for future advancements.