Related Time Series Research Articles

A resilience-oriented multi-stage operation strategy for distribution networks considering multi-type resources

With the increasing severity of global warming and the rapid evolution of cyber-attack techniques, the enhancement of distribution network (DN) resilience that can effectively cope with natural disasters and man-made attacks has received extensive attention. Hence, in the context of a high impact low probability (HILP) event where attackers launch malicious attacks after a natural disaster occurs, a multi-stage DN operation strategy for resilience enhancement is proposed considering multi-type resources, such as gas turbines, stationary energy storage systems, mobile energy storage systems (MESSs), repair crews (RCs) and network reconfiguration. Firstly, the operation process of DN is divided into five stages based on the time lag between the natural disaster and the man-made attacks: the normal operation stage, the disaster disruption stage, the post-disaster degradation stage, the man-made attack stage and the defense and recovery stage. Secondly, under the background of a coupled power-transportation system, the optimal scheduling model for each stage of DN is established considering the effects of dynamic changes in traffic flow on the movement process of MESSs and RCs. Finally, the model is converted into a 5-layer mixed integer linear programming (MILP) problem that can be solved by sophisticated optimization software according to the time series relationship, and a case study is carried out with a modified IEEE 33-node system and the corresponding transportation network. The results demonstrate that under extreme scenarios, the proposed model can guarantee the load-side power supply as much as possible through flexible cooperation between multi-type resources, effectively enhancing the resilience of the DN.

The Principles of Modelling Causal Relationships in Financial Time Series on the Example of Stock Indices

In the article, the authors consider the issues of modeling causal relationships using the example of financial time series. Many endogenous indicators with tens and hundreds of feedbacks characterise any complex dynamic system. The links between the elements of the system determine the impact on the result of the system through a variety of parameters that can lead to a distortion of the effect of managerial influence and, as a result, to a distortion of the system's efficiency as a whole. Modelling is an effective tool for determining the considered system parameters under the influence of external forces and restrictions on it.In the activity of the control system, the analytical tools of mathematical modelling based on the developed modelallow to see how the system will change over time and what factors have the most significant impact on it. The system is changeable in time, which causes the difference between the initial state and the state at a certain point. That is explained by causal relationships between the elements of the systems and the level of influence of external factors, which can be referred to as economic or market ones. The latter, in turn, can be analyzed and, as a result, predicted using mathematical methods. Speaking of external factors, many of them are not amenable to precise analysis, and examples of these are natural and ecological ones.In this article, the authors define the principles of modelling causal relationships in financial time series using the example of stock indices. In the course of the study, the unsteadiness of time series was verified by Dickey-Fuller tests, as well as using the KPSS test solution, which confirmed the unsteadiness of stock indices in the selected time period. The calculations were carried out using the modern statistical package Eviews.As a consequence, the relevance of this article is based on the search for an answer to the question of stationary financial time series.

Exploring explicit and implicit graph learning for multivariate time series imputation

Multivariate time series inherently contain missing values due to various issues, including incorrect data entry, broken equipment, and package loss during data transferring. The successful completion of time series data analysis tasks heavily relies on the essential task of imputing missing values. Inter-variable relationships in time series are typically overlooked by missing value imputation techniques. Although some graph-based algorithms can capture these relationships, the design of graph structures is commonly handcrafted and dataset-centric. We introduce a novel Explicit and Implicit Graph Recurrent Network (EIGRN) for multivariate time series imputation that integrates graph and recurrent neural networks to capture variable and time dependencies together. This proficiency is achieved by effectively integrating external data sources such as domain knowledge and the implicit relationships among nodes. In order to make our approach more applicable to datasets with larger numbers of missing values, we additionally discuss the model’s performance for various missing value ratios. Our comprehensive experiments on real-world datasets show that our model outperforms state-of-the-art baselines in different industrial fields.

Analysis of time series importance measure of high-end equipment cellular manufacturing system

Aiming at the problem to difficultly identify the weak links of the cellular manufacturing system, a method for analyzing the importance measure based on the time series is proposed. The method extends the traditional importance measure calculation method at a specific time to the equipment importance measure in a specific time period, and the time series relationship among the equipment reliability, manufacturing cell reliability and system reliability is mined. It uses the slope of linear fitting to evaluate the importance measure of the equipment, and improves the identification ability of key equipment in the manufacturing process of the cellular manufacturing system. Through the sensitivity analysis of equipment degradation parameters, the change rule of the equipment importance measure when the equipment degradation parameters change is summarized, and the effectiveness of the time series importance measure in the identification of the key equipment and key manufacturing cells is verified.

Bayesian network based probabilistic weighted high-order fuzzy time series forecasting

The present article proposes a probabilistic weighted high-order fuzzy time series (FTS) forecasting model employing Bayesian network (BN) to address complex relationships and uncertainty hidden in time series. As considerable FTS forecasting models considers the fuzzy relationships between precedent moments and the consequent moment as the complex relationships in time series, BN structure learning is utilized to discover and model dependence relationships between each moment in time series. The combination of the fuzzy relationships modeled by fuzzy logical relationships and the dependence relationships modeled by the BN provides a comprehensive establishment and representation of the complex relationships inherent in time series data. The proposed FTS forecasting method calculates the fuzzy-probabilistic weights of each fuzzy logical relationship group using the improved fuzzy empirical probabilities to model both aleatoric and epistemic uncertainty in time series. To this end, the improved fuzzy empirical probabilities are formulated by integrating fuzzy empirical probabilities with the BN to incorporate dependence relationships from the original time series into the FTS forecasting procedure. The efficiency of the proposed forecasting model is validated on fourteen publicly available time series. Experimental results confirm the better performance of the proposed method comparing with nine existing FTS models and six numeric models. Hypothesis tests also validate the robustness and reliability of the proposed method.

SAC-ConvLSTM: A novel spatio-temporal deep learning-based approach for a short term power load forecasting

The short-term spatiotemporal load forecasting of power distribution networks has been extensively studied in light of high importance in distribution system control and consumption management. Load forecasting is dependent on temporal and spatial parameters e.g., temperature, rainfall, and land use. Additionally, load time series have spatial autocorrelation in cities. Deep learning has been recently demonstrated to be effective and efficient in time-series and load forecasting in power distribution networks. The present study proposes a new algorithm based on Spatial Auto Correlation and Convolutional Long Short-Term Memory (SAC-ConvLSTM) algorithm via spatiotemporal power load autocorrelation modeling and deep learning. Load time-series signals in a power distribution network are decomposed into sub-signals using the discrete wavelet transform (DWT). Then, the spatio-temporal autocorrelation of sub-signals is calculated, alleviating prediction model complexity using spatial statistics. The areas with significant positive/negative/no significant autocorrelation in each time-series sub-signal are separately used as the input of the SAC-ConvLSTM algorithm. Finally, the sub-signals are reconstructed into the load signals. This algorithm models the spatial relationships in time-series at different time intervals for power consumption forecasting. The proposed methodology was found to outperform the support vector machine (SVM), Long Short Term Memory (LSTM), Convolutional LSTM (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Planar Flow-Based Variational Auto-Encoder (PFVAE) and Federated Averaging (FedAVG) algorithms in short-term power load forecasting for short term forecasting of up to two weeks, based on the evaluation metrics of root-mean-square error (RMSE) and mean absolute error (MAE), producing values of 11.08% and 7.02% respectively.

Exploring and visualizing temporal relations in multivariate time series

This paper introduces an approach to analyzing multivariate time series (MVTS) data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon. The paper focuses on two core challenges: identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior. The proposed approach combines existing methods for univariate pattern extraction, computation of temporal relations according to the Allen’s time interval algebra, visual displays of the temporal relations, and interactive query operations into a cohesive visual analytics workflow. The paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match, illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.

Forged facial video detection framework based on multi-region temporal relationship feature

Face generation and manipulation techniques based on deep learning have enabled the creation of sophisticated forged facial videos, which are indistinguishable by human eyes. However, the illegal use of deep fake technology will have a serious impact on social stability, personal reputation, and even national security. Therefore, the detection technology of fake facial videos is of great significance to protect national security and maintain social order. Although the existing video-based fake face video detection technology has achieved good detection performance on the public fake face video database, there are still the following problems: (1) the existing technology uses a 2D attention mechanism to obtain local region features from face images and lacks a 3D attention mechanism to obtain local area features from face videos; (2) after obtaining local area features, the existing technology is directly used to classify or only model the inter-regional relationship of images without modeling the temporal relationship between regions of the video. This paper proposes a fake facial video detection framework based on multi-region temporal relationship features, including designing a three-dimensional attention mechanism to extract local features of multiple regions of the face from the video. In order to model the time series relationship between different face areas, a time series graph convolution neural network is also introduced to extract the time series relationship features between multiple areas. In order to model the time sequence relationship between different face regions, the convolution neural network of a time sequence diagram is also introduced to extract the characteristics of the time sequence relationship between multiple regions. Through the change characteristics of the time sequence relationship between face regions, the timing inconsistency of the face video is detected so as to determine whether the face has been deeply forged. Through experiments on multiple datasets, the experimental results of the model accuracy test show that the method proposed in the present invention achieves the highest detection accuracy, and the accuracy in the FaceForensics++ (low definition) dataset is 18.19% higher than that of the benchmark method. The experimental results of the generalization ability test show that the method proposed in the present invention achieves the highest generalization performance, and the detection accuracy in the Celeb-DF dataset is 11.92% higher than that of the benchmark method.

Air quality prediction by integrating mechanism model and machine learning model

AQP (Air Quality Prediction) is a very challenging project, and its core issue is how to solve the interaction and influence among meteorological, spatial and temporal factors. To address this central conundrum, we make full use of the characteristics of mechanism model and machine learning and propose a new AQP method based on DM_STGNN (Dynamic Multi-granularity Spatio-temporal Graph Neural Network). This method is the first time to use the air quality model HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) to assist in building a dynamic spatio-temporal graph structure to learn the spatiotemporal relationship of pollutants. DM_STGNN is based on an elaborate encoder-decoder architecture. At the encoder, in order to better mine the spatial dependency, we built a multi-granularity graph structure, used meteorological, time and geographical features to establish node attributes, used well-known HYSPLIT model to dynamically establish the edges among nodes, and used LSTM (Long Short Term Memory) to learn the time-series relationship of pollutant concentrations. At the decoder, in order to better mine the temporal dependency, we built an attention mechanism based LSTM for decoding and AQP. Additionally, in order to efficiently learn the temporal patterns from very long-term historical time series and generate rich contextual information, an unsupervised pre-training model is used to enhance DM_STGNN. The proposed model makes full use of and fully considers the influence of meteorological, spatial and temporal factors, and integrates the advantages of mechanism model and machine learning. On a project-based dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in AQP. We also compare the proposed model with the state-of-the-art AQP methods on the dataset of Yangtze River Delta city group, the experimental results show the appealing performance of our model over competitive baselines.

Modeling Complex Concentration-Discharge Relationships with Generalized Additive Models

Concentration-discharge relationships in water chemical time series can provide important insights into sources, mobilization, and delivery of solutes and particulates into stream networks. The observed relationships are often complex, including nonlinear and hysteretic patterns reflecting seasonal, climatic, and land management changes in biogeochemical release and hydrological transport of solutes and particulates to streams. Using standard single concentration-discharge (c-q) slopes can obscure this wealth of information. In this study, we suggest a new approach using generalized additive models for evaluation of complex c-q patterns in low-frequency water quality data (only monthly or biweekly observations). We used these models to estimate c-q slopes together with their uncertainty, to provide evidence of changes in c-q behaviors and their controls. We estimated c-q slopes for a selection of Swedish streams and evaluated their nonlinear, seasonal, and temporal structure as indicators of changing hydrological or biochemical drivers.

Software Aging Prediction for Cloud Services Using a Gate Recurrent Unit Neural Network Model Based on Time Series Decomposition

Software aging, which is caused by the accumulation of errors in the system and the consumption of computing resources, tends to occur in long-running cloud service software systems. In practice, software aging prediction has proven to be useful in planning the time to trigger rejuvenation because it provides a prior estimate of future resource consumption. However, aging indicators (e.g., physical memory) in cloud may have the characteristics of long-term slow growth, medium-term seasonality variations (alternating peaks and troughs), and short-term irregular fluctuations. Unfortunately, most of the existing aging prediction methods (e.g., a statistical or single machine learning model) only focus on the accuracy of short-term prediction, while lacking the cognition of the medium and long-term variations of aging indicators and their functions in formulating the rejuvenation schedule (e.g., performing rejuvenation when the load is low can minimize interference to users). To address the above problems, this paper proposes a novel hybrid aging prediction framework to work on the prediction of memory resource consumption in cloud by applying a seasonal-trend decomposition procedure based on loess (STL) method and Gate Recurrent Unit (GRU) neural network, called the decomposition-based GRU (DGRU). The effectiveness of DGRU mainly has two aspects. One is the STL method as a preprocessing technology that can extract the trend, seasonality, and residual characteristics from memory utilization data. The other is that these characteristics can be well predicted separately by a well-designed GRU model, which can model the time-series relationship between the data. Experimental results show that our DGRU framework has superior performance compared with its competitors, including seven single models and six hybrid models. Our study illustrates that the DGRU is a promising solution for high-precision software aging prediction.

Temporal relationship between vasopressor and sedative administration and cerebrovascular response in traumatic brain injury: a time-series analysis

BackgroundAlthough vasopressor and sedative agents are commonly used within the intensive care unit to mediate systemic and cerebral physiology, the full impact such agents have on cerebrovascular reactivity remains unclear. Using a prospectively maintained database of high-resolution critical care and physiology, the time-series relationship between vasopressor/sedative administration, and cerebrovascular reactivity was interrogated. Cerebrovascular reactivity was assessed through intracranial pressure and near infrared spectroscopy measures. Using these derived measures, the relationship between hourly dose of medication and hourly index values could be evaluated. The individual medication dose change and their corresponding physiological response was compared. Given the high number of doses of propofol and norepinephrine, a latent profile analysis was used to identify any underlying demographic or variable relationships. Finally, using time-series methodologies of Granger causality and vector impulse response functions, the relationships between the cerebrovascular reactivity derived variables were compared.ResultsFrom this retrospective observational study of 103 TBI patients, the evaluation between the changes in vasopressor or sedative agent dosing and the previously described cerebral physiologies was completed. The assessment of the physiology pre/post infusion agent change resulted in similar overall values (Wilcoxon signed-ranked p value > 0.05). Time series methodologies demonstrated that the basic physiological relationships were identical before and after an infusion agent was changed (Granger causality demonstrated the same directional impact in over 95% of the moments, with response function being graphically identical).ConclusionsThis study suggests that overall, there was a limited association between the changes in vasopressor or sedative agent dosing and the previously described cerebral physiologies including that of cerebrovascular reactivity. Thus, current regimens of administered sedative and vasopressor agents appear to have little to no impact on cerebrovascular reactivity in TBI.

Short-Term Electricity Price Forecasting Using EEMD and GRU-NN

A vital necessity for the reliable operation of the power system as well as for the financial well-being of the consumers is an accurate forecast of the price of electricity. The price of electricity is highly volatile, nonlinear, and subject to seasonal fluctuations. The price series is also affected by electrical market uncertainty and demand, which is strongly reliant on weather and electricity usage time. This study presents a short-term electricity price forecasting method that follows the similar days approach comprising Ensemble Empirical Mode Decomposition technique. The suggested method utilizes a feature selection methodology that combines the Random Forest Regressor and the Gradient Boosting Regressor to determine which features are most important for the machine learning model. To forecast the electricity price, a Gated Recurrent Unit Neural Network (GRU-NN) is employed as the machine learning model. The GRU-NN has the capability of accurately capturing complicated temporal relationships in electricity price time series, which enables it to make correct predictions. To evaluate the validity of the proposed method, data from the PJM electricity market have been used. The simulation results demonstrate that the suggested method is superior to the existing technique, with significantly improved values achieved for both the mean absolute percentage error (MAPE) and the root mean square error (RMSE).

A Multi-Task Learning for Submarine Cable Magnetic Anomaly Recognition

The recognition of submarine cable magnetic anomaly (SCMA) signals is a challenging task in magnetic signal data processing. In this study, a multi-task convolutional neural network (MTCNN) model is proposed to simultaneously recognize abnormal signals and locate abnormal regions. The residual block is added to the shared feature backbone to improve the ability of the network to extract high-level features and maintain the gradient stability of the model in the training process. The long short-term memory (LSTM) block is added to the classification branch task to learn the internal relationship of the magnetic anomaly time series, so as to improve the network’s ability to recognize magnetic anomalies. Our proposed model can accurately recognize the SCMA signals collected in the East China Sea and the South China Sea. The classification accuracy and the ability to locate the abnormal regions are close to the manual labeling of human analysts. The newly developed model can help analysts reduce the probability of missing and misjudging submarine cable magnetic anomalies, improve the efficiency and accuracy of interpretation, and could even be deployed to an unmanned platform to realize the automatic detection of SCMAs.

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results.

Product quality prediction in dense medium coal preparation process based on recurrent neural network

ABSTRACT Dense medium separation is one of the most widely used methods for coal beneficiation, and an accurate estimation of product coal ash is the prerequisites to maximize the resource value. In this work, data pre-cleaning and multi-layer feature learning process of deep learning are explored to predict the product quality more accurately. The research results show that, the deep learning model considering the time series characteristics of process variables can predict the change of product quality better, which could be attributed to the fact that the full considers time series relationship between process data and product quality. Specifically, the prediction value obtained by using a single-layer, multi-unit recurrent neural network model (GRU) is improved with Root Mean Square Error (RMSE) at 0.061, and the RMSE for multi-step prediction (10 min) is 0.262. What’s more, the prediction error gradually decreases and tends to be smooth with increasing time step. It is suggested that the time step used for data reconstruction should be the time of raw coal coming through to the clean coal belt. This work provides an innovative idea for improving online prediction of coal ash and is of significant importance for precise control of dense medium separation in application.

A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction

Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the optimal feature subset to predict best, and the GJO-CLT achieves the best performance for different backtracking windows and prediction steps. These results demonstrate that the prediction system excels in the task of multivariate water quality time series prediction of WWTPs.

The relation between wheat, soybean, and hemp acreage: a Bayesian time series analysis

The 2018 United States Farm Bill has opened the possibility for farmers to increase their profits through hemp cultivation. The literature suggests hemp has the potential to replace soybeans in soybean–wheat double-cropping because hemp shares key attributes of soybeans as a rotation crop (profitability, potential as an energy crop, and maintenance of soil fertility). Nonetheless, due to a short history of hemp cultivation in the USA, it is difficult to predict a time series relationship between hemp, soybean, and wheat through conventional approaches. In this article, we use Bayesian time series models and data from Statistics Canada and the Alberta Agricultural and Rural Development Department to examine a time series relationship between hemp, wheat, and soybean acreage and therefore predict farmers’ decision when hemp is a legal alternative agricultural commodity. Our results show evidence of complementary and substitution relationships for hemp–wheat and hemp–soybean, respectively. In addition, the results indicate a potential of hemp monoculture as a positive response to self-positive shock on hemp acreage that lasts for years.

A Novel Deep Parallel Time-Series Relation Network for Fault Diagnosis

Currently, deep learning based methods are widely used in the fault diagnosis of time series data for their high precision. However, the application of traditional deep learning fault diagnosis models is limited by their calculational efficiency and poor interpretation ability. To address the problems, a fault diagnosis model named the deep parallel time-series relation network (DPTRN) is proposed in this paper. There are three main advantages of the DPTRN. (1) Our proposed time relationship unit can perform feature extraction on each time node of a time series sample simultaneously, therefore, the DPTRN performs fault diagnosis in a parallel way and improves the computing efficiency significantly. (2) By improving the absolute position embedding, our novel decoupling position embedding unit can be directly applied for fault diagnosis and can learn contextual information. (3) Our proposed DPTRN has an obvious advantage in feature interpretability compared with traditional deep learning based models. Applying the DPTRN model on four datasets, we achieved higher diagnosis performance with much lower cost, which indicates the effectiveness, efficiency and interpretability of the proposed DPTRN model.

A novel episodic memory test optimized for smart phone high‐frequency assessments

AbstractBackgroundDecline in episodic memory is a strong marker of neurodegenerative diseases, making it an important target for cognitive assessment. We present a new remote, repeatable, and brief assessment of episodic memory and visual short‐term memory (VSTM) for smart phone deployment. We evaluated this task for sensitivity to age and established cognitive measures of memory and attention.MethodThe task consists of a learning phase where participants see a sequence of items, then are asked to replicate the item order and location by dragging and dropping these items on‐screen. A recall phase follows a minimum 12‐hour delay, with replication of the earlier response. Each session takes less than 2 minutes. The first phase supplies metrics of spatial and order precision VSTM, with the second phase characterising long‐term episodic memory.Experiment 1 (n = 133) compares the task with CANTAB assessments of visuospatial memory. In Experiment 2 (n = 80) the novel task was repeated twice daily for a week, alongside momentary mood ratings, and a more comprehensive battery of CANTAB tasks at baseline.In Experiment 1, multiple regression and dominance analyses explore the independent and shared variance of measures in explaining age. In Experiment 2, clustering and multivariate autoregressive models outline differences in timeseries relationships between mood and memory, and age.ResultExperiment 1 revealed that CANTAB measures and novel spatial and order metrics were correlated with age (r = .20 to r = .37, adjusted P = <.05). Multiple regression showed models with CANTAB Paired Associate Learning (PAL) score, and delayed novel spatial metrics were significantly predictive of age (t = ‐4.08, 3.17, p <.005). The dominance analysis revealed novel immediate precision overlapped with existing measures in predicting Age, while delayed precision had more unique variance (4.6% vs 0.62% unique sample variance explained in Age, accounting for PAL).Experiment 2 characterised variance in individual learning curves over time and differing autoregressive processes associated with age and CANTAB measures.ConclusionWe present a brief novel episodic memory task that can be deployed remotely, with delayed spatial precision significantly predictive of healthy Ageing. Additionally, we show this test’s suitability as a repeated assessment, compatible with high‐frequency study designs in older populations.