Random Time Series Research Articles

EXPRESS: Impact of Dynamic Operations Complexity on Performance: the Role of National Culture

We build on the national culture, complex adaptive systems, and operations complexity literature to develop hypotheses about the moderating role of national culture on the negative relationship between dynamic operations culture and operational complexity. Monthly performance data were collected from 179 plants in a multinational corporation’s global plant network that spans all national culture clusters. We use random effects time series modeling to incorporate the transitory nature of this relationship and control unobserved heterogeneity over time. The results indicate that the relationship between dynamic operations complexity and operational performance is moderated by every dimension of national culture except gender egalitarianism and institutional collectivism. We contribute to the literature on complexity by introducing the dynamic operations complexity construct and showing the moderating effect of national culture. We contribute to the national culture literature by comprehensively examining the role of national culture supporting the national culture divergence thesis. Managerial implications relate to locating new plants in regions whose national culture is supportive of key goals and allocating tasks and products accordingly.

Research on Product Demand Forecasting Based on Random Forest and ARIMA Time Series: Precision Forecasting Method for Data-Scarce Environments

Research on Product Demand Forecasting Based on Random Forest and ARIMA Time Series: Precision Forecasting Method for Data-Scarce Environments

Response spectra of earthquakes using ARMA model

This study is concerned with calculating hysteretic energy using ARMA coefficient models. We know that: For any random time series we can write a model of ARMA(p,q) detailed by Box and Jenkins [2]. Any movement generates energy, regardless of its form or type, negative or positive, so how do we deal with it or reduce its danger? This is possible through ARMA models that have proven their efficiency through several procedures such as earthquakes and accelerometer time series that are generated from random models in the time domain or frequency domain. Extensive reviews of time series process models for acceleration in the frequency and times domain have been presented by [1], [12], and [4]. A number of papers have reported on ARMA models. ARMA models are discussed in detail by Box and Jenkins [4]. Here we generally found the hysteretic energy NH in terms of the ARMA(2,1) coefficients, but the ARMA(2,1) model can be considered the most appropriate in this case for the regions of northern Algeria, from which we took the Boumerdes earthquake. When taking the AIC RITERIA criterion, we found that it is in fact ARMA. (2,1) which is what we studied The use of mathematical relationships, including some results of nodal integration, such as relationship [7] , which conforms to the definition of a stationary time series, which is that the mean and standard deviation are constant.

Expected Values in Complex Networks Constructed Using a Compression Algorithm to Time Series

This paper introduces a methodology for computing expected values associated with compression networks resulting from the application of compression algorithms to independent and identically distributed random time series. Our analysis establishes a robust correspondence between the calculated expected values and empirically derived results obtained from constructing networks using nondeterministic time series. Notably, the ratio of the average indegree of a network to the computed expected indegree for stochastic time series serves as a versatile metric. It enables the assessment of inherent randomness in time series and facilitates the distinction between nondeterministic and chaotic systems. The metric demonstrates high sensitivity to nondeterminism in both synthetic and real-world datasets, highlighting its capacity to detect subtle disturbances and high-frequency noise, even in series characterized by a deficient sample rate. Our results extend and confirm previous findings in the field.

Probabilistic fragility assessment of slopes considering uncertainty associated with temporal patterns of rainfall intensity

The temporal patterns of rainfall intensity, characterized by the variability of rainfall intensity over time during a single rainfall event, play a crucial role in landslide triggering and evolution. However, the influence of temporal patterns of rainfall intensity on the fragility and reliability assessment of slope instability has received limited attention. This study presents a novel methodology for probabilistic slope fragility assessment accounting for uncertainty associated with the temporal patterns of rainfall intensity. Hourly-scale random time series for the rainfall event are generated, considering uncertainties in temporal patterns of rainfall intensity, using the statistical concepts of skewness and kurtosis within the Pearson system of distributions. A time-dependent seepage-stability mechanical model coupling the finite element method and limit equilibrium method for the slope under hourly-scale rainfall time series is established. Slope fragility assessment under rainfall is conducted, accounting for uncertainties in temporal patterns of rainfall intensity based on Monte Carlo simulation. An illustrative example is provided, focusing on a hypothetical soil slope in the southeastern coastal area of China, susceptible to rainfall-induced landslides. The impact of skewness and kurtosis of rainfall time series on the slope failure probability and failure time are examined.

The influence of an experimental physical training program on 100 m running speed indicators of educators of table tennis groups of a higher education institution. Dynamics and forecasting.

The problem of improving the process of physical education of students has been the subject of specialists' attention for many years. Analysis of literary sources shows that the level of physical fitness, mental and moral strength of the main mass of applicant’s education remains quite low. This requires finding new ways to improve physical, mental and moral condition of student youth. Purpose: experimental check the effect of specially designed classes included in the physical education program training of students of sports oriented groups in table tennis of the institution of higher education on the indicator of running speed for 100 m. Material: used in the experiment participation of 106 first-year students of different genders (53 – control group and 53 – experimental). The R/S method was proposed for processing experimental data. Informed consent to participate in this experiment was obtained from all participants. Results: at the end of the experiment (May), the results of the students of the experimental group qualitatively differed from the results of the students of the control group groups The results of the pedagogical experiment indicate the need for inclusion of the proposed classes in the educational program for physical education of students of groups with a sports orientation, in particular (sectional classes) table tennis. The experimental program included classes with running at the maximum pace at short distances from 15 to 30 m, at a distance of 60 and 100 m, running at maximum pace cross step in different directions. Relays with running were also used, moving in different ways, mobile games and sports team games. Organization education seekers was carried out by the frontal method and the method of circular training. When using circular training, accelerations for 100 m were used speed up to 65-70% of the maximum, which alternated with running for a distance of 50 m at a slow pace The training program also included classes with cross-country elements training and elements of basic aerobics, step aerobics, dance aerobics, which accounted for up to 25% of the time. The pedagogical experiment was conducted during the academic year with from October 2020 to June 2021. Conclusions: this experiment proved that that the inclusion of specially developed table tennis sections in the curriculum classes, made it possible to improve the indicators of education seekers in running a distance of 100 m. The method of exponential smoothing made it possible to predict the results on future. One of the advantages of the calculation method proposed in the article is that that fractal analysis allows to reveal stochastic (random) time series. In such, there is no long-term statistical dependence in the time series.

Random and time-persistent depositional processes in turbidite successions: an example from the marine deep-water Aoshima Formation (Neogene, Kyushu Island, southwest Japan)

ABSTRACT The deposits of flood- and earthquake-derived subaqueous sediment gravity flows represent a significant fraction of lacustrine and deep-sea sedimentary successions, thus providing a valuable record of such natural disasters. The magnitude of these events and the thickness of the associated deposits are considered to follow a lognormal or power-law frequency distribution, whilst that of time intervals between subsequent events appear to be best approximated by a Poisson model, indicative of a random, time-independent phenomenon. However, the debate on whether the sedimentary record of these natural disasters is governed by randomness alone or whether there is some underlying stratigraphic ordering is still unsettled and requires detailed time-series analysis. This study consists of a time-series analysis of mudstone- and sandstone-dominated turbidite successions offshore a fan-delta system in the Neogene Aoshima Formation that belongs to the sedimentary fill of the forearc basin of southwest Japan. The formation consists of a monotonous alternation of very fine- to medium-grained sandstones capped by hemipelagic mudstones and, more rarely, by turbidite mudstones. The results show that the autocorrelation function of the time series suggests quasi-periodic variability in the upper sandstone-dominated part, whereas the lower mudstone-dominated part shows a white-noise-like pattern. Rescaled range analysis shows that the number of events per unit time in the lower part is characterized by a random time series, such as Brownian noise with a Hurst exponent of 0.5. In contrast, the thickness of event beds of the lower part and the thickness and the number of events of the upper part are persistent time series with a Hurst exponent > 0.5. These results suggest that the number of turbidite depositional events in the mudstone-dominated part indicates random timing, whereas its thickness time series and the sandstone-dominant part are not governed by simple stochastic processes but are affected by sea-level changes, sediment transport dynamics, and other factors such as, for example, seafloor topography.

Intuitionistic fuzzy time series forecasting method based on dendrite neuron model and exponential smoothing

Methods based on artificial neural networks for intuitionistic fuzzy time series forecasting can produce successful forecasting results. In the literature, exponential smoothing methods are hybridised with artificial neural networks due to their simple and efficient structures to improve the forecasting performance. The contribution of this paper is to propose a new forecasting approach combining exponential smoothing methods and intuitionistic fuzzy time series. In this study, a forecasting algorithm based on the dendrite neuron model and simple exponential smoothing methods is proposed for modelling intuitionistic fuzzy time series. In the fuzzification stage of the proposed method, the intuitionistic fuzzy c-means method is used. The proposed method is a modular method using two separate dendrite neuron model neural networks and the grey wolf optimisation algorithm is used to estimate all parameters of the method. The performance of the proposed method is applied on four different random time series obtained for Index of Coin Market Cap and the performance of the method is compared with some other fuzzy forecasting methods. As a result of the analyses, it is concluded that the proposed modular method has better forecasting results than other methods.

Distinction of Chaos from Randomness Is Not Possible from the Degree Distribution of the Visibility and Phase Space Reconstruction Graphs.

We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies.

Spatially Explicit Active Learning for Crop-Type Mapping from Satellite Image Time Series.

The availability of a sufficient number of annotated samples is one of the main challenges of the supervised methods used to classify crop types from remote sensing images. Creating these samples is time-consuming and costly. Active Learning (AL) offers a solution by streamlining sample annotation, resulting in more efficient training with less effort. Unfortunately, most of the developed AL methods overlook spatial information inherent in remote sensing images. We propose a novel spatially explicit AL that uses the semi-variogram to identify and discard redundant, spatially adjacent samples. It was evaluated using Random Forest (RF) and Sentinel-2 Satellite Image Time Series in two study areas from the Netherlands and Belgium. In the Netherlands, the spatially explicit AL selected 97 samples achieving an overall accuracy of 80%, compared to traditional AL selecting 169 samples with 82% overall accuracy. In Belgium, spatially explicit AL selected 223 samples and obtained 60% overall accuracy, while traditional AL selected 327 samples and obtained an overall accuracy of 63%. We concluded that the developed AL method helped RF achieve a good performance mostly for the classes consisting of individual crops with a relatively distinctive growth pattern such as sugar beets or cereals. Aggregated classes such as 'fruits and nuts' posed, however, a challenge.

Leveraging Large Data, Statistics, and Machine Learning to Predict the Emergence of Resistant E. coli Infections.

Drug-resistant Gram-negative bacterial infections, on average, increase the length of stay (LOS) in U.S. hospitals by 5 days, translating to approximately $15,000 per patient. We used statistical and machine-learning models to explore the relationship between antibiotic usage and antibiotic resistance over time and to predict the clinical and financial costs associated with resistant E. coli infections. We acquired data on antibiotic utilization and the resistance/sensitivity of 4776 microbial cultures at a Kaiser Permanente facility from April 2013 to December 2019. The ARIMA (autoregressive integrated moving average), neural networks, and random forest time series algorithms were employed to model antibiotic resistance trends. The models' performance was evaluated using mean absolute error (MAE) and root mean squared error (RMSE). The best performing model was then used to predict antibiotic resistance rates for the year 2020. The ARIMA model with cefazolin, followed by the one with cephalexin, provided the lowest RMSE and MAE values without signs of overfitting across training and test datasets. The study showed that reducing cefazolin usage could decrease the rate of resistant E. coli infections. Although piperacillin/tazobactam did not perform as well as cefazolin in our time series models, it performed reasonably well and, due to its broad spectrum, might be a practical target for interventions in antimicrobial stewardship programs (ASPs), at least for this particular facility. While a more generalized model could be developed with data from multiple facilities, this study acts as a framework for ASP clinicians to adopt statistical and machine-learning approaches, using region-specific data to make effective interventions.

Evaluating machine learning algorithms to predict lameness in dairy cattle.

Dairy cattle lameness represents one of the common concerns in intensive and commercial dairy farms. Lameness is characterized by gait-related behavioral changes in cows and multiple approaches are being utilized to associate these changes with lameness conditions including data from accelerometers, and other precision technologies. The objective was to evaluate the use of machine learning algorithms for the identification of lameness conditions in dairy cattle. In this study, 310 multiparous Holstein dairy cows from a herd in Northern Colorado were affixed with a leg-based accelerometer (Icerobotics® Inc, Edinburg, Scotland) to obtain the lying time (min/d), daily steps count (n/d), and daily change (n/d). Subsequently, study cows were monitored for 4 months and cows submitted for claw trimming (CT) were differentiated as receiving corrective claw trimming (CCT) or as being diagnosed with a lameness disorder and consequent therapeutic claw trimming (TCT) by a certified hoof trimmer. Cows not submitted to CT were considered healthy controls. A median filter was applied to smoothen the data by reducing inherent variability. Three different machine learning (ML) models were defined to fit each algorithm which included the conventional features (containing daily lying, daily steps, and daily change derived from the accelerometer), slope features (containing features extracted from each variable in Conventional feature), or all features (3 simple features and 3 slope features). Random forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Time series (ROCKET) were used as ML predictive approaches. For the classification of cows requiring CCT and TCT, ROCKET classifier performed better with accuracy (> 90%), ROC-AUC (> 74%), and F1 score (> 0.61) as compared to other algorithms. Slope features derived in this study increased the efficiency of algorithms as the better-performing models included All features explored. However, further classification of diseases into infectious and non-infectious events was not effective because none of the algorithms presented satisfactory model accuracy parameters. For the classification of observed cow locomotion scores into severely lame and moderately lame conditions, the ROCKET classifier demonstrated satisfactory accuracy (> 0.85), ROC-AUC (> 0.68), and F1 scores (> 0.44). We conclude that ML models using accelerometer data are helpful in the identification of lameness in cows but need further research to increase the granularity and accuracy of classification.

PreGLAM: A Predictive, Gameplay-based Layered Affect Model

We present the Predictive Gameplay-based Layered Affect Model (PreGLAM), an affective game spectator model that flexibly integrates into a game design process. PreGLAM combines elements of real-time Player Experience Models, and Affective Non-Player-Character models to output real-time estimated values for a spectator's valence, arousal, and tension during gameplay. Because tension is related to prospective events, PreGLAM attempts to predict future gameplay events. We implement and evaluate PreGLAM in a custom game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Galactic Defense</i> , which we also describe. PreGLAM significantly outperforms a random walk time series in how accurately it matches ground-truth annotations, and has comparable accuracy to state of the art affect models.

Mapping time series into signed networks via horizontal visibility graph

Time series could be mapped into complex networks through the visibility or horizontal visibility algorithms, and the properties of the constructed network reflect the nonlinear dynamics of the time series. When horizontal visibility algorithm is directly applied to climate anomaly time series, in which both local maximum and local minimum are equally important, local minimum might be “overlooked”. In this paper, we propose a new method that maps climate anomaly time series into signed networks. Positive and negative data values of climate anomaly time series are classified into two types and mapped as nodes of signed networks. Links connecting nodes of the same type are assigned positive signs, while links connecting neighboring nodes of different types are assigned negative signs. This method is also applicable to time series those are assumed to be “stationary” or with no significant trends. Four kinds of degree as well as the degree distributions of the signed networks have been defined. Specifically, the degree and degree distribution could be partly derived analytically for periodic and uncorrelated random time series. The theoretical predictions for periodic and uncorrelated random time series have also been verified by extensive numerical simulations. Based on the entropy of the distribution of net degree, we propose a new complexity measure for chaotic time series. Compared to some previous complexity measures, the new complexity measure is an objective measure without transforming continuous values into discrete probability distributions but still has higher accuracy and sensitivity. Moreover, correlation information of stochastic time series can also be extracted via a topological parameter, the mean of ratio degree, of the signed networks. The extraction of serial correlation has been illustrated through numerical simulations and verified through an empirical climate time series.

Record ages of non-Markovian scale-invariant random walks

How long is needed for an observable to exceed its previous highest value and establish a new record? This time, known as the age of a record plays a crucial role in quantifying record statistics. Until now, general methods for determining record age statistics have been limited to observations of either independent random variables or successive positions of a Markovian (memoryless) random walk. Here we develop a theoretical framework to determine record age statistics in the presence of memory effects for continuous non-smooth processes that are asymptotically scale-invariant. Our theoretical predictions are confirmed by numerical simulations and experimental realisations of diverse representative non-Markovian random walk models and real time series with memory effects, in fields as diverse as genomics, climatology, hydrology, geology and computer science. Our results reveal the crucial role of the number of records already achieved in time series and change our view on analysing record statistics.

Framework of wind joint analysis for different lake regions and its effects on the water quality

Water quality of Lake Taihu (LT) is paid wide attention, and the wind field makes wind-induced flow, significantly impacting the water quality. As a wide lake, the wind field suffers spatial and temporal changes. The correlated relationship among the wind fields for different lake regions has not been studied, and the joint effects of wind field on the water quality is still unknown. Hence, this paper proposed a framework of wind joint analysis for different lake regions and modelling its effects on the water quality, including joint analysis of wind field for different lake regions using Copula function, random time series generation of wind field using Monte-Carlo simulation, and hydrodynamic and water quality numerical simulation. Taking the Lake Taihu (LT) as study case, the joint relationships of wind field for four lake regions were analyzed, and the CODMN, TP and TN distributions under different wind fields were simulated. This paper showed the following: (1) The relationship between the wind speed and direction was weak, and the correlated relationships among the wind fields for four lake regions were significant (ρ > 0.5). (2) The water quality of LT was more influenced by the wind direction rather than the wind speed, with an impacting ratio of ∼4.5. (3) The discharged weighted angle of runoff outflow (DWARO) was a practical index to improve the water quality. When regulating the input/output runoff discharge, water diversion project and water intakes in practice, the administrations should make the DWARO less than or equal to the average wind direction, to decrease the average pollutant concentration and improve the water quality of lake and intake. The proposed framework and results of LT could be extended to other wide shallow lakes to support the environmental management and basin runoff regulation.

Consistent Model Selection Procedure for Random Coefficient INAR Models.

In the realm of time series data analysis, information criteria constructed on the basis of likelihood functions serve as crucial instruments for determining the appropriate lag order. However, the intricate structure of random coefficient integer-valued time series models, which are founded on thinning operators, complicates the establishment of likelihood functions. Consequently, employing information criteria such as AIC and BIC for model selection becomes problematic. This study introduces an innovative methodology that formulates a penalized criterion by utilizing the estimation equation within conditional least squares estimation, effectively addressing the aforementioned challenge. Initially, the asymptotic properties of the penalized criterion are derived, followed by a numerical simulation study and a comparative analysis. The findings from both theoretical examinations and simulation investigations reveal that this novel approach consistently selects variables under relatively relaxed conditions. Lastly, the applications of this method to infectious disease data and seismic frequency data produce satisfactory outcomes.

Kinetic model derived from machine learning for accurate prediction of microalgal hydrogen production via conversion from low thermally pre-treated palm kernel expeller waste

The depletion of fossil fuel sources and increase in energy demands have increased the need for a sustainable alternative energy source. The ability to produce hydrogen from microalgae is generating a lot of attention in both academia and industry. Due to complex production procedures, the commercial production of microalgal biohydrogen is not yet practical. Developing the most optimum microalgal hydrogen production process is also very laborious and expensive as proven from the experimental measurement. Therefore, this research project intended to analyse the random time series dataset collected during microalgal hydrogen productions while using various low thermally pre-treated palm kernel expeller (PKE) waste via machine learning (ML) approach. The analysis of collected dataset allowed the derivation of an enhanced kinetic model based on the Gompertz model amidst the dark fermentative hydrogen production that integrated thermal pre-treatment duration as a function within the model. The optimum microalgal hydrogen production attained with the enhanced kinetic model was 387.1 mL/g microalgae after 6 days with 1 h thermally pre-treated PKE waste at 90 °C. The enhanced model also had better accuracy (R2 = 0.9556) and net energy ratio (NER) value (0.71) than previous studies. Finally, the NER could be further improved to 0.91 when the microalgal culture was reused, heralding the potential application of ML in optimizing the microalgal hydrogen production process.

Combined forecasting approach for product quality based on support vector regression and gray forecasting model

Forecasting product quality by incorporating customer satisfaction perception factors is an intriguing research area, which can promote the sustainable development of enterprises. To address small-sample random time series data, this study proposes a combined forecasting approach (CFA) for the product quality index that considers perception factors. The proposed approach is based on the support vector regression (SVR) and an improved gray forecasting model (GFM). First, the study constructs a system of perception factors related to defect parts per million (DPPM). Then, the key perception factors (KPF) are selected using the gray entropy relational degree, which is derived from gray relational analysis and information entropy. Then, a multivariable GFM is proposed based on the weighted Markov and the derived form of the gray model to reduce the forecasting error. Finally, a CFA is constructed considering KPF and optimized based on the SVR and the proposed GFM to forecast the DPPM. A case study of liquid crystal display is conducted to demonstrate the feasibility of the proposed CFA. The forecast error of the proposed CFA is 3.2%, which is better than those of GFM, SVR, and ARIMA (4.01%, 6.21%, and 9.89%, respectively). The comparison and discussion of methods demonstrate the superiority of the proposed approach for forecasting product quality.

EFFICIENCY OF THE INDIAN STOCK MARKET - AN EMPIRICAL STUDY

Market efficiency has an influence on the investment strategy of an investor because if the market is efficient, trying to pick undervalued security will be a waste of time. In an efficient market, there will be no undervalued securities offering higher than deserved expected returns, given their risk. On the other hand, if markets are not efficient, excess returns can be made by correctly picking the security. In this paper, an analysis of five popular stocks is carried out to test the efficiency level of the Indian Stock market in semi-strong form and the random walk nature of the stock market by using the event analysis and t-test for the period from 1st April 2016 to 31st March 2017. The study carried out in this paper has presented evidence of the inefficient form of the Indian Stock Market in a semi-strong form. From the event analysis and t-test, we are able to conclude that the series of stocks in the Indian Stock Market is biased random time series. It indicates that the behaviour of share prices does not confirm the applicability of the random walk model in the Indian stock market. Thus, undervalued securities are in the market, and the investors can always get excess returns by correctly picking them.