Analysis Of Time Series Data Research Articles

THE IMPACT OF THE RUSSIAN-UKRAINIAN WAR ON THE FOOD INFLATION RATE PERSISTENCE IN UKRAINE: A FRACTIONAL INTEGRATION MODEL

This study seeks to test for food inflation persistence in Ukraine using univariate fractional integration techniques. The GPH (Geweke-Porter-Hudak) test and the local Whittle estimator are both methods used in the analysis of time series data, particularly in the context of long memory processes. In addition to measuring the persistence of the food inflation rate using the ARFIMA model, the empirical results suggest evidence of the persistence of low food inflation rate in Ukraine, albeit with the same trend, before and during the Russian-Ukrainian war. Also, the food inflation rate has long-term memory properties, regardless of the sample used. The results show that memory parameters for all cases (Full, Pre-war, and Dur-war) are within the stationary range (0 < d < 0.5) and with almost equal. Nevertheless, the estimates of d suggest that the food inflation rate series exhibit a long memory, that the effect of shocks will eventually disappear in the short run in all cases (Full, Pre-war, and Dur-war). So there is no strong impact of the Russian-Ukrainian war on the food inflation rate persistence in Ukraine. This explains that the Ukrainian government's monetary policy strongly absorbs the shocks and effectively mitigates the impacts of the Russian-Ukrainian war. The ability to also exploit fractional integration in a univariate setup when modeling food inflation is a major contribution of this study, and ignoring the same may lead to wrong conclusions.

Macroeconomic and demographic impacts on Islamic life insurance demand in Indonesia

Purpose – This study examines the influence of macroeconomics and population demographics on demand for Islamic life insurance in Indonesia.Methodology – This study uses time-series data analysis using the autoregressive distributed lag (ARDL)method. The study period is from January 2015 to December 2022.Findings – According to the ARDL model, inflation and education levels have a positive effect on the demand for Islamic life insurance in the long run, while the Muslim population has a negative effect. In the short term, gross domestic product (GDP) per capita has a positive effect, while inflation and the Muslim population have a negative effect on the demand for Islamic life insurance in Indonesia.Implications – This study provides valuable insights for the Islamic life insurance industry and policymakers. The industry should develop inclusive and affordable products tailored to diverse financial capacities and preferences, including those of the younger generations. Policymakers should promote public awareness and collaboration with financial institutions in order to expand access. These findings can guide strategies to enhance market penetration and financial inclusion.Originality – This study fills the research gap by analyzing the relationship between Islamic life insurance demand, macroeconomics, and population demographics, where Muslim population has never been discussed by previous studies.

Time series data analysis and association rule mining in financial recommendation systems using Hadoop and Spark

Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models. This article discusses combining time series analysis and association rule mining for big data in Hadoop and Spark to enrich financial product recommendation engines. The paper is an integrated analysis of two types of prediction algorithms: AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks to forecast user behavior and demand for financial services in the future from transactional history. The ARIMA model is used as the default while the LSTM model is used to represent non-linear dependencies and give a more dynamic forecast. association rule mining in particular the Apriori algorithm is used to find latent patterns and relationships between user transactions and financial products. This article illustrates how time series forecasting and association rule mining can be merged to bring a more useful financial recommendation. The hybrid approach, which combines both approaches, proves to increase user interaction and recommendation accuracy by 20% compared to the previous systems, according to experiments. The paper emphasises the possibilities of using big data in the construction of scalable, individualized financial recommendation systems.

The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation study.

Due to global climate change, high temperature and heatwaves have become critical issues that pose a threat to human health. An effective early warning system is essential to mitigate the health risks associated with high temperature and heatwaves. However, most of the current heatwave early warning systems are not adequately developed based on the heat-health risk model, and the health impact of hot weather has not been well managed in most countries. This study proposed a "full-season coverage and population health-oriented graded early-warning" concept and developed a heat-health surveillance, forecast and early warning (HHSEW) model. The exposure-response (E-R) relationship between temperature and mortality was analyzed through a two-stage approach using time-series analysis data from 323 counties across China for the period 2013-2018. The premature mortality curve at each temperature percentile was plotted and four temperature-percentile points on the curve were determined as the thresholds of the pre-warning and warning levels 1-3 based on the variations in the rates of the segmental slopes on the curve. The HHSEW model was evaluated by comparing the frequency, the mortality risk of all-cause and cause-specific diseases, the predicted numbers of premature deaths, and the heat-related health economic burden at each warning level with those of the current high temperature early warning systems. The HHSEW model determined five levels, including seasonal surveillance, pre-warning, and warning levels 1-3. There was a gradual increase in the mortality risks of all-cause and cause-specific diseases along with the increase of warning levels. The risk of all-cause mortality increased by 9.79% (95% CI: 8.59%-11.01%), 22.62% (95% CI: 19.49%-25.83%), 28.36% (95% CI: 24.72%-32.10%), and 33.87% (95% CI: 28.89%-39.06%) at the pre-warning level, warning level 1, warning level 2, and warning level 3, respectively. Through our HHSEW model, 94,008 heat-related all-cause deaths were predicted annually in the 337 major cities of China, which was much larger than the number (14,858) of the China Meteorological Administration (CMA) heatwave early-warning system currently used in China. It was estimated that the proper implementation of the HHSEW-based early warning system would save 220 billion CNY in heat-related health burden compared to the current heatwave early-warning system. The HHSEW model has been proven to surpass the current heatwave early warning system. With its full-season coverage and graded warning levels for heat-related health risks, the HHSEW model and system can provide timely early warnings to the public, leading to significant health benefits. This methodology, labeled "full-season coverage and population health-oriented graded early-warning", should be implemented globally to mitigate the escalating health risks associated with high temperature. National Natural Science Foundation of China (82425051, 42071433, 42305196, 82241051) and the Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology of China (2017FY101204).

Fueling economies through credit and industrial activities. A way of financing sustainable economic development in Brazil

<p>As an emerging country preoccupied with preserving its resources for the future, Brazil aims to find the right balance between economic advancement and sustainability goals. In this context, we tackled the link between sustainable economic development and macroeconomic indicators related to domestic credit granted to the private sector, CO<sub>2</sub> emissions from industries, and annual inflation rate. By means of time series data analysis run for the period 1996‒2022 via three estimation methods (i.e., least squares, fully modified least squares, dynamic least squares), we found that annual GDP growth rate, control of corruption, and rule of law (as proxies for sustainable economic development) are significantly impacted by GDP growth rate and emissions. Therefore, access to financial resources and intensive industrial activities yielding emissions trigger economic growth, tend to strengthen control of corruption and the rule of law. Additionally, policy implications and future research directions are addressed.</p>

Exploring the Interface Between Planetary Boundaries and Palaeoecology.

The concepts of planetary boundaries are influential in the sustainability literature and assist in delineating the 'safe operating spaces' beyond which critical Earth system processes could collapse. Moving away from our current trajectory towards 'hothouse Earth' will require knowledge of how Earth systems have varied throughout the Holocene, and whether and how far we have deviated from past ranges of variability. Such information can inform decisions about where change could be resisted, accepted or where adaptation is inevitable. The need for information on long-term (Holocene) change provides an interface for palaeoecology and sustainability that remains underexploited. In this position paper, we explore this interface, first discussing the need for long-term perspectives and introducing examples where palaeoecology has been used in defining safe operating spaces and constraining limits of acceptable change. We describe advances in quantitative methods for analysis of time-series data that strengthen the contribution of palaeoecology to the concepts of planetary boundaries and safe operating spaces. We consider the importance of issues of scaling from landscape to regional and global scales in operationalising planetary boundaries concepts. We distil principles for this field of research going forward and introduce three case studies which will form the basis of research on these topics.

The financial crisis, economic development and business management in Pakistan: An Evidence through time-series data analysis in Pakistan

The aim of this research work is to analyze the causal linkage among Pakistan's economic development and the three main financial crisis indicators—interest rates, inflation, and the amount of foreign debt. Additionally, this analysis emphasizes how consistently financial crisis indicators and economic growth. The analysis makes use of sample size covering data from 2000 to 2024. The convergence of equilibrium for series utilized in the work is examined using Johansen's co-integration test. The findings show that the three elements of Pakistan's financial crisis and economic growth have overall causal relationship. The business, economic and each financial crisis indicator taken into consideration in this study have a bidirectional causal relationship, according to estimations based on the pair-wise Granger Causality test. Whereas, the management of economy is determined on the economic and business parameters as analyzed on data which provides a sometimes unidirectional or bidirectional indication for solution of economic crisis and way for economic development

Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM

BackgroundMissing observations within the univariate time series are common in real-life and cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in every incomplete univariate time series. Most of the existing studies focus on comparing the distributions of imputed data. There is a gap of knowledge on how different imputation methods for univariate time series affect the forecasting performance of time series models. We evaluated the prediction performance of autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network models on imputed time series data using ten different imputation techniques.MethodsMissing values were generated under missing completely at random (MCAR) mechanism at 10%, 15%, 25%, and 35% rates of missingness using complete data of 24-h ambulatory diastolic blood pressure readings. The performance of the mean, Kalman filtering, linear, spline, and Stineman interpolations, exponentially weighted moving average (EWMA), simple moving average (SMA), k-nearest neighborhood (KNN), and last-observation-carried-forward (LOCF) imputation techniques on the time series structure and the prediction performance of the LSTM and ARIMA models were compared on imputed and original data.ResultsAll imputation techniques either increased or decreased the data autocorrelation and with this affected the forecasting performance of the ARIMA and LSTM algorithms. The best imputation technique did not guarantee better predictions obtained on the imputed data. The mean imputation, LOCF, KNN, Stineman, and cubic spline interpolations methods performed better for a small rate of missingness. Interpolation with EWMA and Kalman filtering yielded consistent performances across all scenarios of missingness. Disregarding the imputation methods, the LSTM resulted with a slightly better predictive accuracy among the best performing ARIMA and LSTM models; otherwise, the results varied. In our small sample, ARIMA tended to perform better on data with higher autocorrelation.ConclusionsWe recommend to the researchers that they consider Kalman smoothing techniques, interpolation techniques (linear, spline, and Stineman), moving average techniques (SMA and EWMA) for imputing univariate time series data as they perform well on both data distribution and forecasting with ARIMA and LSTM models. The LSTM slightly outperforms ARIMA models, however, for small samples, ARIMA is simpler and faster to execute.

Greening the Cityscape: Strategies for Sustainable Urbanization, Low Carbon Emissions, and Robust Economic Growth

This study thoroughly investigates the intricate association concerning urbanization, energy consumption, carbon emissions, and economic growth in Saudi Arabia from 1968 to 2022. Using advanced econometric models and time-series data analysis, we reveal a complex association in which economic growth and urbanization influence energy consumption, leading to increased carbon emissions. The adjustment coefficients for this relationship are estimated to be 0.16%. The causality test findings revealed the existence of both unidirectional and bidirectional causality. Urbanization has a direct impact on both energy consumption and carbon emissions. In turn, carbon emissions affect both energy use and economic growth. Ultimately, carbon emissions influence both energy consumption and economic growth. Our results highlight the urgent need for strategies to reduce carbon emissions by implementing sustainable energy practices, urban planning, and legislative measures. These implications for sustainable development are crucial for Saudi Arabia and offer a structure for other rapidly urbanizing nations dealing with comparable difficulties. Given the potential for change, policymakers must carefully consider and implement these results.

Analysis of Crisis Effects via Maximum Entropy Adjustment

ABSTRACTCrises, such as the Covid‐19 epidemic, can affect economic time series by distorting historical trends and seasonal patterns with extreme values. The identification and adjustment of such extremes is important for the production of seasonally adjusted data; an ongoing challenge is the identification of a crisis' end, where the time series returns to more typical pre‐crisis dynamics. A secondary challenge is to model and analyze time series data with both missing values and extremes. This paper extends the maximum entropy framework to a generalized class of extreme values, including level shifts, temporary changes, and seasonal outliers. These outliers are described as a particular type of stochastic process that is latent, or unobserved, such that its removal increases the time series entropy. The proposed methods allow one to model and fit time series data using conventional tools in the presence of specified streams of extreme values, as well as missing values. Extreme value adjustment, with quantification of mean squared error, can then be obtained along with the seasonal adjustment; there is also a test statistic to directly compare two specifications of extremes. The techniques are illustrated in weekly employment data.

Transient Deformation in the Tatun Volcano Group, Taiwan: A Spatiotemporal GPS Analysis

AbstractThis study analyzed time series data from six GPS stations within the Tatun Volcano Group (TVG), a long‐dormant volcanic system in northern Taiwan, using multichannel singular spectrum analysis to search for potential spatiotemporally correlated transient deformations. A notable cycle of transient deformation was identified from 2015 to 2020, characterized by ground subsidence and uplift of up to 10 mm, accompanied by asymmetric horizontal motions directed inward and outward toward Dayoukeng, the largest fumarole and hydrothermal area in TVG. Evidence from earthquake focal mechanisms and gas composition, along with preliminary source modeling, suggest that these transient phases were likely caused by the pressure change of shallow hydrothermal systems beneath Dayoukeng. Further analysis of time series data from three long‐operating GPS stations revealed similar patterns of transient motion in the area from 2006 to 2015, indicating that TVG has experienced cyclical deformation, akin to many other volcanic systems worldwide.

From classroom to industry: Developing universal competencies and AI integration in engineering education for technological advancements

The purpose of this study is to develop strategies for enhancing higher and postgraduate education in Kazakhstan, with a focus on improving graduate readiness for professional roles and building predictive models to analyze and assess doctoral graduation rates in technical fields for 2023–2025. This research emphasizes a flexible framework that integrates universal competencies into engineering programs, adaptable to various institutional and national contexts. Active learning methods, including project-based tasks, problem-solving exercises, internships, and lab work, are employed to cultivate practical skills aligned with real-world engineering demands. The study also highlights the need for comprehensive assessment systems that extend beyond theoretical testing, incorporating continuous feedback and curriculum adjustments to align with labor market needs and scientific developments. Results reveal key areas for higher education reform, as outlined in Kazakhstan’s State Program for Education and Science Development (2020–2025). These include implementing a competency-based approach, optimizing interdisciplinary and practice-oriented teaching methods, and enhancing the global competitiveness of Kazakhstani universities. Additionally, through time-series analysis of doctoral graduation data, a trend model was created, showing a steady increase in graduates, tested for anomalies and trend adequacy, making it reliable for forecasting graduate numbers. The scientific significance of this work lies in its contributions to models that improve graduate training, fostering high-quality education to enhance Kazakhstani universities’ global competitiveness. By integrating AI-driven strategies and emerging technologies, the research provides an adaptive educational model to equip students with essential professional and universal competencies, essential for navigating the evolving labor market and supporting sustainable development.

Gait Analysis through Animated GIFs: A Novel Approach for Visualizing Step Dynamics

The field of prosthetic knee development has made great progress in recent years, notably with the use of machine learning (ML) approaches. Researchers made significant progress in enhancing the functionality, flexibility, and user experience of prosthetic knees using revolutionary machine learning techniques.Since gait analysis delivers part of the most important information about human motion patterns, it becomes critical in the development of a prosthetic knee. In this paper, some new techniques for gait analysis with feature extraction are proposed that include visualization using animated GIF images. This methodology extracts key parameters like step length and step width from the frames of a GIF image and can completely represent the dynamics of gait with respect to time.This methodology is particularly significant to design an efficient prosthetic knee since it shows how combining computer analysis and visual data may enhance gait analysis methods. For this study, brief walking footage of both healthy individuals and amputees are recorded, then turned into GIFs. Subsequently, these GIF data points are retrieved, making it possible to quantify step length and other essential metrics required to analyses gait patterns.Gait analysis is incorporated as part of the development process for these prosthetic knee joints due to the fact that it gives information on human motion patterns. The methods used in this paper provide novel ways of carrying out gait analysis through feature extraction and visualization using animated GIF images. This method would help in depicting the full picture of gait dynamics with time and assist in extracting important parameters such as step length and width from the frames of the GIF. The work, as described, falls squarely into the use of computer vision techniques for feature extraction and into time-series data analysis for trend identification within the broader application of machine learning in the development of the prosthetic knee. These techniques could be generalized toward designing better prosthetic knee design for more natural and fluent motion.

Analysis of the Influence of Open Unemployment Rate, Road Infrastructure Development, Inflation, and Economic Growth on Poverty Levels in Mataram City (2010-2023)

Although economic growth had started to improve at the time due to the World Superbike (WSBK) event being held in Mandalika, the rising poverty rate in Mataram has continued to fluctuate. For example, in 2021, the city's poverty rate increased. Economic growth, inflation, and the construction of transportation infrastructure all partially and concurrently contribute to Mataram City's poverty rate. Descriptive and associative quantitative methods are used in this study. Using multiple linear regression analysis methodologies, secondary and time series data from According to the study's findings, just one factor—inflation—had a substantial impact on the poverty rate in Mataram, whereas the open unemployment rate, road length, and economic growth had no discernible effects. The poverty rate in the city of Mataram is significantly impacted by the open unemployment rate, road length, inflation, and economic growth all at the same time. The development of MSMEs based on the creative economy, the creation of an appropriate city road network that will be more beneficial to the community's economy, targeted fiscal policies to lower the rate of inflation, and other suggestions are some ways the Mataram city government can try to alleviate poverty, and designing the right strategy to optimize foreign trade.

Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery Data.

Type 1 diabetes (T1D) is a chronic condition in which the body produces too little insulin, a hormone needed to regulate blood glucose. Various factors such as carbohydrates, exercise, and hormones impact insulin needs. Beyond carbohydrates, most factors remain underexplored. Regulating insulin is a complex control task that can go wrong and cause blood glucose levels to fall outside a range that protects people from adverse health effects. Automated insulin delivery (AID) has been shown to maintain blood glucose levels within a narrow range. Beyond clinical outcomes, data from AID systems are little researched; such systems can provide data-driven insights to improve the understanding and treatment of T1D. The aim is to discover unexpected temporal patterns in insulin needs and to analyze how frequently these occur. Unexpected patterns are situations where increased insulin does not result in lower glucose or where increased carbohydrate intake does not raise glucose levels. Such situations suggest that factors beyond carbohydrates influence insulin needs. We analyzed time series data on insulin on board (IOB), carbohydrates on board (COB), and interstitial glucose (IG) from 29 participants using the OpenAPS AID system. Pattern frequency in hours, days (grouped via k-means clustering), weekdays, and months were determined by comparing the 95% CI of the mean differences between temporal units. Associations between pattern frequency and demographic variables were examined. Significant differences in IOB, COB, and IG across temporal dichotomies were assessed using Mann-Whitney U tests. Effect sizes and Euclidean distances between variables were calculated. Finally, the forecastability of IOB, COB, and IG for the clustered days was analyzed using Granger causality. On average, 13.5 participants had unexpected patterns and 9.9 had expected patterns. The patterns were more pronounced (d>0.94) when comparing hours of the day and similar days than when comparing days of the week or months (0.3<d<0.52). Notably, 11 participants exhibited a higher IG overnight despite concurrently higher IOB (10/11). Additionally, 17 participants experienced an increase in IG after COB decreased after meals. The significant associations between pattern frequency and demographics were moderate (0.31≤τ≤0.48). Between clusters, mean IOB (P=.03, d=0.7) and IG (P=.02, d=0.67) differed significantly, but COB did not (P=.08, d=0.55). IOB and IG were most similar (mean distance 5.08, SD 2.25), while COB and IG were most different (mean distance 11.43, SD 2.6), suggesting that AID attempts to counteract both observed and unobserved factors that impact IG. Our study shows that unexpected patterns in the insulin needs of people with T1D are as common as expected patterns. Unexpected patterns cannot be explained by carbohydrates alone. Our results highlight the complexity of glucose regulation and emphasize the need for personalized treatment approaches. Further research is needed to identify and quantify the factors that cause these patterns.

The performance of the LSTM-based code generated by Large Language Models (LLMs) in forecasting time series data

Generative AI, and in particular Large Language Models (LLMs), have gained substantial momentum due to their wide applications in various disciplines. While the use of these game changing technologies in generating textual information has already been demonstrated in several application domains, their abilities in generating complex models and executable codes need to be explored. As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep learning models and codes and thus may opt to leverage LLMs to generate the required models. This paper investigates and compares the performance of the mainstream LLMs, such as ChatGPT, PaLM, LLama, and Falcon, in generating deep learning models for analyzing time series data, an important and popular data type with its prevalent applications in many application domains including financial and stock market. This research conducts a set of controlled experiments where the prompts for generating deep learning-based models are controlled with respect to sensitivity levels of four criteria including (1) Clarify and Specificity, (2) Objective and Intent, (3) Contextual Information, and (4) Format and Style. While the results are relatively mix, we observe some distinct patterns. We notice that using LLMs, we are able to generate deep learning-based models with executable codes for each dataset separately whose performance are comparable with the manually crafted and optimized LSTM models for predicting the whole time series dataset. We also noticed that ChatGPT outperforms the other LLMs in generating more accurate models. Furthermore, we observed that the goodness of the generated models vary with respect to the “temperature” parameter used in configuring LLMS. The results can be beneficial for data analysts and practitioners who would like to leverage generative AIs to produce good prediction models with acceptable goodness.

Self-Exciting Threshold Autoregressive (SETAR) Modelling of the NSE 20 Share Index Using the Bayesian Approach

The analysis and interpretation of time series data is of great importance across different fields, including economics, finance, and engineering, among other fields. This kind of data, characterized by sequential observations over time, sometimes exhibits complex patterns and trends that some commonly used models, such as linear autoregressive (AR) and simple moving average (MA) models, cannot capture. This limitation calls for the development of more sophisticated and flexible models that can effectively capture the complexity of time series data. In this study, a more sophisticated model, the Self-Exciting Threshold Autoregressive (SETAR) model, is used to model the Nairobi Securities Exchange (NSE) 20 Share Index, incorporating a Bayesian parameter estimation approach. The objectives of this study are to analyze the properties of the NSE 20 Share Index data, to determine the estimates of SETAR model parameters using the Bayesian approach, to forecast the NSE 20 Share Index for the next 12 months using the fitted model, and to compare the forecasting performance of the Bayesian SETAR with the frequentist SETAR and ARIMA model. Markov Chain Monte Carlo (MCMC) techniques, that is, Gibbs sampling and the Metropolis-Hastings Algorithm, are used to estimate the model parameters. SETAR (2; 4, 4) model is fitted and used to forecast the NSE 20 Share Index. The study&amp;apos;s findings generally reveal an upward trajectory in the NSE 20 Share Index starting September 2024. Even though a slight decline is predicted in November, an upward trend is predicted in the following months. On comparing the performance of the models, the Bayesian SETAR model performed better than the linear ARIMA model for both short and longer forecasting horizons. It also performed better than its counterpart model, which uses the frequentist approach for a longer forecasting horizon. These results show the applicability of SETAR modeling in capturing non-linear dynamics. The Bayesian approach incorporated for parameter estimation advanced the model even further by providing a flexible and robust way of parameter estimation and accommodating uncertainty.

Identifying Ordinal Similarities at Different Temporal Scales

This study implements the permutation Jensen–Shannon distance as a metric for discerning ordinal patterns and similarities across multiple temporal scales in time series data. Initially, we present a numerically controlled analysis to validate the multiscale capabilities of this method. Subsequently, we apply our methodology to a complex photonic system, showcasing its practical utility in a real-world scenario. Our findings suggest that this approach is a powerful tool for identifying the precise temporal scales at which two distinct time series exhibit ordinal similarity. Given its robustness, we anticipate that this method could be widely applicable across various scientific disciplines, offering a new lens through which to analyze time series data.

Dynamic mode decomposition for analysis of time-series data

Dynamic mode decomposition for analysis of time-series data

Object oriented data analysis of surface motion time series in peatland landscapes

Abstract Peatlands account for 10% of UK land area, 80% of which are degraded to some degree, emitting carbon at a similar magnitude to oil refineries or landfill sites. A lack of tools for rapid and reliable assessment of peatland condition has limited monitoring of vast areas of peatland and prevented targeting areas urgently needing action to halt further degradation. Measured using interferometric synthetic aperture radar (InSAR), peatland surface motion is highly indicative of peatland condition, largely driven by the eco-hydrological change in the peatland causing swelling and shrinking of the peat substrate. The computational intensity of recent methods using InSAR time series to capture the annual functional structure of peatland surface motion becomes increasingly challenging as the sample size increases. Instead, we utilize the behaviour of the entire peatland surface motion time series using object oriented data analysis to assess peatland condition. Bayesian cluster analysis based on the functional structure of the surface motion time series finds areas indicative of soft/wet peatlands, drier/shrubby peatlands, and thin/modified peatlands. The posterior distribution of the assigned peatland types enables the scale of peatland degradation to be assessed, which will guide future cost-effective decisions for peatland restoration.