Time Series Analysis Approach Research Articles

Individual Response to Botulinum Toxin Therapy in Movement Disorders: A Time Series Analysis Approach.

On a group level, satisfaction with botulinum neurotoxin (BoNT) treatment in neurological indications is high. However, it is well known that a relevant amount of patients may not respond as expected. The aim of this study is to evaluate the BoNT treatment outcome on an individual level using a statistical single-case analysis as an adjunct to traditional group statistics. The course of the daily perceived severity of symptoms across a BoNT cycle was analyzed in 20 cervical dystonia (CD) and 15 hemifacial spasm (HFS) patients. A parametric single-case autoregressive integrated moving average (ARIMA) time series analysis was used to detect individual responsiveness to BoNT treatment. Overall, both CD and HFS patients significantly responded to BoNT treatment with a gradual worsening of symptom intensities towards BoNT reinjection. However, only 8/20 CD patients (40%) and 5/15 HFS patients (33.3%) displayed the expected U-shaped curve of BoNT efficacy across a single treatment cycle. CD (but not HFS) patients who followed the expected outcome course had longer BoNT injection intervals, showed a better match to objective symptom assessments, and were characterized by a stronger certainty to control their somatic symptoms (i.e., internal medical locus of control). In addition to standard evaluation procedures, patients should be identified who do not follow the mean course-of-treatment effect. Thus, the ARIMA single-case time series analysis seems to be an appropriate addition to clinical treatment studies in order to detect individual courses of subjective symptom intensities.

Assessing the eco-efficiency of complex forestry enterprises using LCA/time-series DEA methodology

Complex forestry enterprises have the potential to lead the current industrial upgrade under the strategy of carbon neutrality and carbon peaking. This paper aims to investigate the inputs and outputs influencing the eco-efficiency of complex forestry enterprises through a case study. A hybrid ensemble approach of life cycle assessment (LCA) and time-series data envelopment analysis (time-series DEA) was proposed to evaluate the case enterprise's eco-efficiency. It is found that, from the perspective of the average intensity of key input and output factors, environmental impact in efficient years is 45.25% of that in inefficient ones, and when it comes to the fiber consumption and the energy consumption, this percentage turns into 65.53% and 77.66%. With the findings, we provide guidelines for complex forestry enterprises to enhance the eco-efficiency in improving environmental impact and in giving full play to the advantages of forestry industrial chain resources. Our findings also contribute to the existent literature by providing a systematic study based on a corporate time-series dataset.

Structural damage detection using low-rank matrix approximation and cointegration analysis

This paper proposes a novel approach of time series analysis to identify the potential changes in structural conditions, e.g., degradation owing to accumulated damage. Although the damage-sensitive features (DSFs) of structures depend on the environmental and operational conditions and thus vary over time, they usually have a common trend when the effects of environmental and operational variations (EOVs) are linear or quasi-linear. Therefore, cointegration analysis, which can combine several time series into a stationary residual purged of the common trend, is used to remove the effects of EOVs and assess the structural condition. The main contribution of this study is that a low rank matrix approximation (LRMA) algorithm is introduced to constrain the rank of the stacked DSF matrix, thereby suppressing the random errors inevitable in structural damage detection and both the linear and nonlinear effects induced by EOVs. The nonlinear effects have been observed in the identified natural frequency data of several bridges in service and cannot be easily handled by the general cointegration due to its linear nature. Another advantage of this process is that the missing entities in the DSF series can be automatically imputed, taking full advantage of incomplete data acquired for analysis. The effectiveness of the proposed method is demonstrated by using the benchmark data of the KW51 Railway Bridge in structural condition identification. Results indicate that the changes in the structural condition can be correctly detected despite the existence of random errors and nonlinear effects induced by EOVs. The proposed method can also work when only a small amount of incomplete data is available.

A guiding role of the Arabidopsis circadian clock in cell differentiation revealed by time-series single-cell RNA sequencing.

Circadian rhythms and progression of cell differentiation are closely coupled in multicellular organisms. However, whether establishment of circadian rhythms regulates cell differentiation or vice versa has not been elucidated due to technical limitations. Here, we exploit high cell fate plasticity of plant cells to perform single-cell RNA sequencing during the entire process of cell differentiation. By analyzing reconstructed actual time series of the differentiation processes at single-cell resolution using a method we developed (PeakMatch), we find that the expression profile of clock genes is changed prior to cell differentiation, including induction of the clock gene LUX ARRYTHMO (LUX). ChIP sequencing analysis reveals that LUX induction in early differentiating cells directly targets genes involved in cell-cycle progression to regulate cell differentiation. Taken together, these results not only reveal a guiding role of the plant circadian clock in cell differentiation but also provide an approach for time-series analysis at single-cell resolution.

Toward Carbon-Neutral Water Systems: Insights from Global Cities

Many cities have pledged to achieve carbon neutrality. The urban water industry can also contribute its share to a carbon-neutral future. Using a multi-city time-series analysis approach, this study aims to assess the progress and lessons learned from the greenhouse gas (GHG) emissions management of urban water systems in four global cities: Amsterdam, Melbourne, New York City, and Tokyo. These cities are advanced in setting GHG emissions reduction targets and reporting GHG emissions in their water industries. All four cities have reduced the GHG emissions in their water industries, compared with those from more than a decade ago (i.e., the latest three-year moving averages are 13%–32% lower), although the emissions have “rebounded” multiple times over the years. The emissions reductions were mainly due to various engineering opportunities such as solar and mini-hydro power generation, biogas valorization, sludge digestion and incineration optimization, and aeration system optimization. These cities have recognized the many challenges in reaching carbon-neutrality goals, which include fluctuating water demand and rainfall, more carbon-intensive flood-prevention and water-supply strategies, meeting new air and water quality standards, and revising GHG emissions accounting methods. This study has also shown that it is difficult for the water industry to achieve carbon neutrality on its own. A collaborative approach with other sectors is needed when aiming toward the city’s carbon-neutrality goal. Such an approach involves expanding the usual system boundary of the water industry to externally tap into both engineering and non-engineering opportunities.

A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP-STSA) for Emotion Recognition Using EEG Signals.

Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically. There are many approaches due to different features extractions methods for analyzing the EEG signals. Still, Since the brain is supposed to be a nonlinear dynamic system, it seems a nonlinear dynamic analysis tool may yield more convenient results. A novel approach in Symbolic Time Series Analysis (STSA) for signal phase space partitioning and symbol sequence generating is introduced in this study. Symbolic sequences have been produced by means of spherical partitioning of phase space; then, they have been compared and classified based on the maximum value of a similarity index. Obtaining the automatic independent emotion recognition EEG-based system has always been discussed because of the subject-dependent content of emotion. Here we introduce a subject-independent protocol to solve the generalization problem. To prove our method’s effectiveness, we used the DEAP dataset, and we reached an accuracy of 98.44% for classifying happiness from sadness (two- emotion groups). It was 93.75% for three (happiness, sadness, and joy), 89.06% for four (happiness, sadness, joy, and terrible), and 85% for five emotional groups (happiness, sadness, joy, terrible and mellow). According to these results, it is evident that our subject-independent method is more accurate rather than many other methods in different studies. In addition, a subject-independent method has been proposed in this study, which is not considered in most of the studies in this field.

Damage detection of wind turbine blades by Bayesian multivariate cointegration

Changes in the vibration responses of wind turbine blades (WTBs) can be used to detect the presence of damages. However, constantly changing environmental circumstances might cause vibration responses to change, complicating the damage detection procedure. This study introduces an approach of time series analysis termed Bayesian cointegration for damage detection. This approach is extended from a univariate to a multivariate case, allowing for the simultaneous inclusion of more than two damage-sensitive features (DSFs) for analysis. The common trend induced by environmental fluctuations in the DSFs series is purged using the Bayesian cointegration approach. As a result, structural degradation can be detected when the underlying long-run equilibrium relationship among the time series suffers noticeable changes. The proposed approach has clear advantages over the frequently used Johansen test for cointegration: (1) Artificial parameter selection is unnecessary because all involved parameters can be inferred probabilistically; (2) It has a greater capacity to capture possible cointegrating relationships. Benchmark data from a numerical WTB are used to demonstrate the performance of the proposed method. Results show that this method can effectively eliminate the influence of environmental variations and detect the progressive damage of the WTB.

Proactive Prevention Model to Manage Construction Time Delays in Developing Countries

Due to the complex structure of construction activities, schedule overruns are inevitable in the construction industry. Particularly, construction projects play a key role in the economic conditions of developing countries. Although several studies have mainly focused on the time delay factors in the construction projects, a study does not exist on providing reliable and applicable solutions based on the eye of expert view. Thus, this study attempts not only to identify the major delay factors but also to introduce a proactive prevention model including long-term strategies and immediate practical solutions to be implemented in the construction industry of developing countries. For this purpose, a survey was conducted with 140 experts in the construction sector of a developing country, Iraq. Importance Index (Imp. I) based on the frequency and severity value of each time delay attribute and Relative Importance Index (RII) for suggestions and proactive steps were calculated according to survey results. After identifying major causal factors, strategies, and practical implementations, we developed a proactive prevention model to be utilized in the construction industry. The proposed model includes different practical implementations for ongoing construction projects such as developing an Artificial Intelligence (AI)-based predictive model, using Time Series Analysis (TSA) approach, urgent practical applications, and long term strategies for construction companies to reduce time overruns in construction projects. To realize this kind of proactive prevention model in the construction industry, a data collection and sharing system including all related information recorded in diverse construction projects is highly recommended.

A New ANN Approach for Time Series Analysis

Time series analysis and accurate forecasting of energy prices are critical for both policymakers and market participants. In the practical analysis of price time series, the coefficients play vital roles; however, their accurate estimation is a challenging issue as they are affected by external factors. This study proposes a new modelling approach for Artificial Neural Networks (ANNs) models based on fuzzy logic. For this purpose, we reformulated an ANN model as a fuzzy nonlinear regression model to capture the advantages of both fuzzy regression and ANN methodologies. This clear-box model can be applied to not only uncertain, ambiguous, and complex environments, but it is also capable of modelling nonlinear patterns. To illustrate the capability of the proposed approach, we report a case study of liquified natural gas (LNG) prices in Japan’s market (the world’s largest natural gas importer). The results support that the performance of the proposed approach is acceptable; moreover, it can deal with uncertain and complex environments as a clear-box model.

Do ridesharing transportation services alleviate traffic crashes? A time series analysis

Objectives On-demand ridesharing services are suggested to provide several benefits, such as improving accessibility and mobility, reducing drive-alone trips and greenhouse gas emissions. However, the impacts of these services on traffic crashes are not completely clear. This paper investigates the availability of Via- an on-demand ridesharing service in Arlington, TX, to identify the effects of this service on traffic crashes. We hypothesize that the launch of Via would result in more shared rides, fewer drive-alone trips and fewer traffic crashes. Methods We implement an Interrupted Time Series Analysis (ITSA) approach to study the impact of Via service availability on traffic crashes using weekly counts of all traffic crashes, the number of injuries, and serious injuries that occurred in Arlington from 2014 to 2021. Results The results show a statistically significant reduction in the weekly number of total crashes and total injuries but do not show any significant impact on the number of serious injuries. Shared Autonomous Vehicles have the potential to reduce traffic crashes caused by driver's fault. Conclusions This study reveals the potential impacts ridesharing services can have on traffic crashes and injuries in a mid-sized city. The results of this study can help decision and policymakers to understand the full potential of ridesharing services that can contribute to making relevant decisions toward creating sustainable and safer transportation systems in cities.

Multi-scale transition network approaches for nonlinear time series analysis

Complex networks are powerful tools for nonlinear time series analysis, which are undergoing fast development in the recent decade. Here we propose a novel way to construct multi-scale transition networks from time series, which are based on coarse-graining partitions of phase space. Using time series from both discrete Hénon map and continuous Rössler systems, we demonstrate that the multi-scale transition entropy values of the resulting networks show the same power as the Lyapunov exponents, identifying chaotic transitions successfully. The advantage is that our method works successfully when only a small number of 3–5 bins is used for the partition generation, while the traditional static node entropy measures work poorly. Further experimental examples in fMRI and ECG analysis show that these entropy measures are able to characterizing different rhythmic states of subjects, showing high potential for time series analysis from complex systems.

Microbial communities across activated sludge plants show recurring species-level seasonal patterns

Microbial communities in activated sludge (AS) are the core of sanitation in wastewater treatment plants (WWTPs). Microbial communities in AS have shown seasonal changes, however, long-term experiments (>2 years) are rarely conducted, limiting our understanding of the true seasonal dynamics in WWTPs. In this study, we resolved the microbial seasonal dynamics at the species level in four municipal full-scale WWTPs, sampled every 7–10 days, during 3–5 consecutive years. By applying a new time-series analysis approach, we revealed that the seasonal pattern was species-specific, where species belonging to the same functional guild or genus may show different seasonal dynamics. Species could be grouped into cohorts according to their seasonal patterns, where seasonal cohorts showed repeatable annual dynamics across years and plants. Species were also grouped according to their net growth rate in the AS (i.e., growing species and disappearing species). Growing species were more prevailing in spring and autumn cohorts, while disappearing species, which were only present due to the continuous immigration from influent wastewater, were mostly associated with winter and spring cohorts. Most known process-critical species, such as nitrifiers, polyphosphate accumulating organisms and filamentous organisms, showed distinct species-specific patterns. Overall, our study showed that overarching seasonal patterns affected microbial species in full-scale AS plants, with similar seasonal patterns across plants for many dominant species. These recurrent seasonal variations should be taken into account in the operation, understanding and management of the WWTPs.

White Noise Test from Ordinal Patterns in the Entropy–Complexity Plane

SummaryThis article serves two purposes. Firstly, it surveys the Bandt and Pompe methodology for the statistical community, stressing topics that are open for research. Secondly, it contributes towards a better understanding of the statistical properties of that approach for time series analysis. The Bandt and Pompe methodology consists of computing information theory descriptors from the histogram of ordinal patterns. Such descriptors lie in a 2D manifold: the entropy–complexity plane. This article provides the first proposal of a test in the entropy–complexity plane for the white noise hypothesis. Our test is based on true white noise sequences obtained from physical devices. The proposed methodology provides consistent results: It assesses sequences of true random samples as random (adequate test size), rejects correlated and contaminated sequences (sound test power) and captures the randomness of generators previously analysed in the literature.

Spatio-temporal evolution of the magma plumbing system at Masaya Caldera, Nicaragua

Volcanic unrest in calderas can be exhibited through a variety of different mechanisms, such as changes in seismicity and ground deformation, as well as variations in thermal and/or gas emissions. However, not all caldera unrest results in explosive caldera-forming volcanic activity. Alternative activity may include periods of quiescence, passive degassing, effusive activity (e.g., lava flows lava lakes and dome formation), and/or magma injection into the shallow magma system. In this study, we perform a long-term study (spanning 2011–2019) of ground deformation at Masaya using six Interferometric Synthetic Aperture Radar (InSAR) datasets. Masaya exhibited bi-modal eruptive behavior between 2011 and 2019, dominated by open-vent lava lake activity and punctuated by short-lived summit explosions. The Multidimensional Small BAseline Subset time-series analysis approach was used to take advantage of the temporally dense SAR datasets. Between 2012 and early 2015, we observed degassing-induced pressurization of the Masaya Central Reservoir (MCR) at an estimated volume change rate of ~ 0.28 $$\times$$ 106 m3/year. In May 2015, magma was supplied into the MCR at a rate of ~ 5.6 $$\times$$ 106 m3/year, leading to the appearance of a summit lava lake in December 2015. Over the next 6 months, rapid magma supply continued to drive lava lake activity and was followed by a cessation of magma supply into the MCR for another 11 months. From mid-2017 to end-2019, we observed depressurization (~ − 0.67 $$\times$$ 106 m3/year) of the MCR due to a lack of magma supply and continued high rates of degassing in-conjunction with declining lava lake activity.

Autoregressive matrix factorization for imputation and forecasting of spatiotemporal structural monitoring time series

Reconstruction and prediction of spatiotemporal time series data has been a classic problem in structural health monitoring (SHM) in civil engineering applications. However, due to the explosive growth of sensing data, traditional time series analysis approaches fail in handling large-scale data with missing values. To this end, two autoregressive (AR) based matrix factorization (MF) methods are presented for missing sensor data imputation and structural response forecasting. The first model integrates the standard MF formulation with an innovative graph-based temporal regularizer, which can effectively model the nonlinear dynamics of SHM data and is computationally efficient, while the second approach introduces an additional AR-based matrix to better simulate the temporal factor thanks to its capability of learning the details of temporal evolution. Finally, the proposed methods are evaluated by using a field-recorded SHM dataset of a municipal concrete bridge, considering various missing scenarios (i.e., random, structured and mixed). The results demonstrate excellent performance of the methods which accurately recover missing entries in the time series and forecast future response. Additionally, the parametric analysis on model parameters indicates that reasonably higher rank and longer time lag improve the estimation accuracy while saving computational cost.

Environmental Controls on Multi-Scale Dynamics of Net Carbon Dioxide Exchange From an Alpine Peatland on the Eastern Qinghai-Tibet Plateau.

Peatlands are characterized by their large carbon storage capacity and play an essential role in the global carbon cycle. However, the future of the carbon stored in peatland ecosystems under a changing climate remains unclear. In this study, based on the eddy covariance technique, we investigated the net ecosystem CO2 exchange (NEE) and its controlling factors of the Hongyuan peatland, which is a part of the Ruoergai peatland on the eastern Qinghai-Tibet Plateau (QTP). Our results show that the Hongyuan alpine peatland was a CO2 sink with an annual NEE of −226.61 and −185.35 g C m–2 in 2014 and 2015, respectively. While, the non-growing season NEE was 53.35 and 75.08 g C m–2 in 2014 and 2015, suggesting that non-growing seasons carbon emissions should not be neglected. Clear diurnal variation in NEE was observed during the observation period, with the maximum CO2 uptake appearing at 12:30 (Beijing time, UTC+8). The Q10 value of the non-growing season in 2014 and 2015 was significantly higher than that in the growing season, which suggested that the CO2 flux in the non-growing season was more sensitive to warming than that in the growing season. We investigated the multi-scale temporal variations in NEE during the growing season using wavelet analysis. On daily timescales, photosynthetically active radiation was the primary driver of NEE. Seasonal variation in NEE was mainly driven by soil temperature. The amount of precipitation was more responsible for annual variation of NEE. The increasing number of precipitation event was associated with increasing annual carbon uptake. This study highlights the need for continuous eddy covariance measurements and time series analysis approaches to deepen our understanding of the temporal variability in NEE and multi-scale correlation between NEE and environmental factors.

A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer.

In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.

Medium-term prediction of key chemical process parameter trend with small data

Accidents might happen in chemical factories when less experienced operators unreasonably react to emergencies. It is therefore desirable to predict key process parameters early enough to help plant management easily and readily assess the coming risks and take proper actions to avoid them. Some of the predictions are in the time scale of days, if not months. However, such tools are rarely available in the chemical process industry, and often require a large amount of data for modeling. With inspiration from ecological researches, we propose a novel integrated causal analysis multiview embedding(ICAME) algorithm to predict medium-term trends of key chemical process parameters with small datasets. The proposed ICAME is mainly based on empirical dynamic modeling(EDM), a group of time series analysis approaches. Its application in a simulated dataset and a real industrial process demonstrated its advantages on small data over other methods for medium-term trend predictions of chemical process parameters.

Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device.

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.

Nonlinear time series analysis of palaeoclimate proxy records

Identifying and characterising dynamical regime shifts, critical transitions or potential tipping points in palaeoclimate time series is relevant for improving the understanding of often highly nonlinear Earth system dynamics. Beyond linear changes in time series properties such as mean, variance, or trend, these nonlinear regime shifts can manifest as changes in signal predictability, regularity, complexity, or higher-order stochastic properties such as multi-stability. In recent years, several classes of methods have been put forward to study these critical transitions in time series data that are based on concepts from nonlinear dynamics, complex systems science, information theory, and stochastic analysis. These include approaches such as phase space-based recurrence plots and recurrence networks, visibility graphs, order pattern-based entropies, and stochastic modelling. Here, we review and compare in detail several prominent methods from these fields by applying them to the same set of marine palaeoclimate proxy records of African climate variations during the past 5 million years. Applying these methods, we observe notable nonlinear transitions in palaeoclimate dynamics in these marine proxy records and discuss them in the context of important climate events and regimes such as phases of intensified Walker circulation, marine isotope stage M2, the onset of northern hemisphere glaciation and the mid-Pleistocene transition. We find that the studied approaches complement each other by allowing us to point out distinct aspects of dynamical regime shifts in palaeoclimate time series. We also detect significant correlations of these nonlinear regime shift indicators with variations of Earth's orbit, suggesting the latter as potential triggers of nonlinear transitions in palaeoclimate. Overall, the presented study underlines the potentials of nonlinear time series analysis approaches to provide complementary information on dynamical regime shifts in palaeoclimate and their driving processes that cannot be revealed by linear statistics or eyeball inspection of the data alone.