Periodic Time Series Research Articles

Diagnostic checking of periodic vector autoregressive time series models with dependent errors

In this article, we study the asymptotic behaviour of the residual autocorrelations for periodic vector autoregressive time series models (PVAR henceforth) with uncorrelated but dependent innovations (i.e., weak PVAR). We then deduce the asymptotic distribution of the Ljung–Box-McLeod modified Portmanteau statistics for weak PVAR models. In Monte Carlo experiments, we illustrate that the proposed test statistics have reasonable finite sample performance. When the innovations exhibit conditional heteroscedasticity or other forms of dependence, it appears that the standard test statistics (under independent and identically distributed innovations) are generally unreliable, overrejecting, or underrejecting severely, while the proposed test statistics offer satisfactory levels. The proposed methodology is employed in the analysis of two river flows.

A variational approach to quantum gated recurrent units

Quantum Recurrent Neural Networks are receiving an increased attention thanks to their enhanced generalization capabilities in time series analysis. However, their performances were bottlenecked by long training times and unscalable architectures. In this paper, we propose a novel Quantum Recurrent Neural Network model based on Quantum Gated Recurrent Units. It uses a learnable Variational Quantum Layer to process temporal data, interspersed with two classical layers to properly match the dimensionality of the input and output vectors. Such an architecture has fewer quantum parameters than existing Quantum Long Short-Term Memory models. Both the quantum networks were evaluated on periodic and real-world time series datasets, together with the classical counterparts. The quantum models exhibited superior performances compared to the classical ones in all the test cases. The Quantum Gated Recurrent Units outperformed the Quantum Long Short-Term Memory network despite having a simpler internal configuration. Moreover, the Quantum Gated Recurrent Units network demonstrated to be about 25% faster during the training and inference procedure over the Quantum Long Short-Term Memory. This improvement in speed comes with one less quantum circuit to be executed, suggesting that our model may offer a more efficient alternative for implementing Quantum Recurrent Neural Networks on both simulated and real quantum hardware.

Monitoring resilience in bursts

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

The BT-SAM-Net: a new framework of end-to-end periodic time-series fault diagnosis for aero-pipelines systems

Accurate recognition of aero-engine pipeline faults is of great significance for engine maintenance costs and downtime. Pipeline signals have a strong periodic time series correlation under strong pump source pressure pulsation stimulation. However, very few studies have considered the correlation of features at pulsation period time points. Additionally, it is challenging to realize intelligent fault diagnosis of weak characteristics of pipeline faults due to the influence of vibration-noise coupling of aero-engines. The time information feature extraction model combined with self-attention mechanism (BT-SAM-Net), a newly created fault detection framework end-to-end time-series and anti-noise, is presented for the aero-pipeline in order to close the aforementioned research gaps. The primary goal of the proposed framework is to accomplish intelligent classification tasks by using the measured aero-pipeline raw data as the model input. Firstly, a two-way time series information fusion model is creatively designed, which is the first attempt to analyze the difference in time series correlation characteristics of faults for aero-pipelines. Secondly, The BT-SAM-Net framework incorporates the self-attention mechanism as an optimization tool to enhance the ultimate decision-making accuracy of the framework. Thirdly, the BT-SAM-Net framework was compared with 7 other methods. The results show the superiority and stability by demonstrating the BT-SAM-Net framework can identify various aero-pipeline fault states with greater accuracy.

Analysis of depressive EEG signals via symbolic phase transfer entropy with an adaptive template method

Depression is a prevalent mental disorder in contemporary society. Symbolic phase transfer entropy can quantify the dynamic interaction and information flow between electroencephalogram (EEG) signals in depressed patients and healthy groups, which can help diagnose and treat depression. However, the traditional symbolization process of symbolic phase transfer entropy adopts the basic template method, which makes the symbolic phase transfer entropy unable to express the characteristics and changes of time series in different time periods in detail. Therefore, this paper proposes an improved symbolic phase transfer entropy algorithm, which adopts the adaptive template method in the symbolization process of the symbolic phase transfer entropy algorithm so that it can capture the dynamic changes of time series more finely. It was verified on the task EEG signals of 40 depressed patients and 40 healthy people. The experimental results show that the improved symbolic phase transfer entropy can more accurately distinguish depressed patients from healthy people in lead F4 and lead O1, which is helpful for the study of the EEG pathological characteristics of depression. The improved symbolic phase transfer entropy algorithm makes up for the shortcomings of the traditional symbolic phase transfer entropy in capturing the dynamic changes of time series and provides help for the study of dynamic changes in complex systems.

A framework for the interpretation of heart rate variability applied to transcutaneous auricular vagus nerve stimulation and osteopathic manipulation.

Reports on autonomic responses to transcutaneous auricular vagus nerve stimulation (taVNS) and osteopathic manipulative techniques have been equivocal, partly due to inconsistent interpretation of heart rate variability (HRV). We developed a mechanistic framework for the interpretation of HRV based on a model of sinus node automaticity that considers autonomic effects on Phase 3 repolarization and Phase 4 depolarization of the sinoatrial action potential. The model was applied to HRV parameters calculated from ECG recordings (healthy adult humans, both genders) before (30 min), during (15 min), and after (30 min) a time control intervention (rest, n = 23), taVNS (10 Hz, 300 μs, 1-2 mA, cymba concha, left ear, n = 12), or occipitoatlantal decompression (OA-D, n = 14). The experimental protocol was repeated on 3 consecutive days. The model simulation revealed that low frequency (LF) HRV best predicts sympathetic tone when calculated from heart rate time series, while high frequency (HF) HRV best predicts parasympathetic tone when calculated from heart period time series. Applying our model to the HRV responses to taVNS and OA-D, revealed that taVNS increases cardiac parasympathetic tone, while OA-D elicits a mild decrease in cardiac sympathetic tone.

ERORUN‐STAFOR: A collaborative observatory for the multidisciplinary study of the critical zone processes in a tropical volcanic watershed including a Tropical Montane Cloud Forest

AbstractTropical volcanic islands are biodiversity hotspots where the Critical Zone (CZ) still remains poorly studied. In such steep topographic environments associated with extreme climatic events (cyclones), deployment and maintenance of monitoring equipment is highly challenging. While a few Critical Zone Observatories (CZOS) are located in tropical volcanic regions, none of them includes a Tropical Montane Cloud Forest (TMCF) at the watershed scale. We present here the dataset of the first observatory from the French network of critical zone observatories (OZCAR) located in an insular tropical and volcanic context, integrating a ‘Tropical Montane Cloud Forest’: The ERORUN‐STAFOR observatory. This collaborative observatory is located in the northern part of La Réunion island (Indian Ocean) within the 45.0 km2 watershed of Rivière des Pluies (i.e., Rainfall river) which hosts the TMCF of Plaines des Fougères, one of the best preserved natural habitats in La Réunion Island. Since 2014, the ERORUN‐STAFOR monitoring in collaboration with local partners collected a multidisciplinary dataset with a constant improvement of the instrumentation over time. At the watershed scale and in its vicinity, the ERORUN‐STAFOR observatory includes 10 measurement stations covering the upstream, midstream and downstream part of the watershed. The stations record a total of 48 different variables through continuous (sensors) or periodic (sampling) monitoring. The dataset consists of continuous time series variables related to (i) meteorology, including precipitation, air temperature, relative humidity, wind speed and direction, net radiation, atmospheric pressure, cloud water flux, irradiance, leaf wetness and soil temperature, (ii) hydrology, including water level and temperature, discharge and electrical conductivity (EC) of stream, (iii) hydrogeology, including (ground)water level, water temperature and EC in two piezometers and one horizontally drilled groundwater gallery completed by soil moisture measurements under the canopy. The dataset is completed by periodic time series variables related to (iv) hydrogeochemistry, including field parameters and water analysis results. The periodic sampling survey provides chemical and isotopic compositions of rainfall, groundwater, and stream water at different locations of this watershed. The ERORUN‐STAFOR monitoring dataset extends from 2014 to 2022 with an acquisition frequency from 10 min to hourly for the sensor variables and from weekly to monthly frequency for the sampling. Despite the frequent maintenance of the monitoring sites, several data gaps exist due to the remote location of some sites and instrument destruction by cyclones. Preliminary results show that the Rivière des Pluies watershed is characterized by high annual precipitation (>3000 mm y−1) and a fast hydrologic response to precipitation (≈2 h basin lag time). The long‐term evolution of the deep groundwater recharge is mainly driven by the occurrence of cyclone events with a seasonal groundwater response. The water chemical results support existing hydrogeological conceptual models suggesting a deep infiltration of the upstream infiltrated rainfall. The TMCF of Plaine des Fougères shows a high water storage capacity (>2000% for the Bryophytes) that makes this one a significant input of water to groundwater recharge which still needs to be quantified. This observatory is a unique research site in an insular volcanic tropical environment offering three windows of observation for the study of critical zone processes through upstream‐midstream‐downstream measurements sites. This high‐resolution dataset is valuable to assess the response of volcanic tropical watersheds and aquifers at both event and long‐term scales (i.e., global change). It will also provide insights in the hydrogeological conceptual model of volcanic islands, including the significant role of the TMCFs in the recharge processes as well as the watershed hydrosedimentary responses to extreme climatic events and their respective evolution under changing climatic conditions. All data sets are available at https://doi.org/10.5281/zenodo.7983138.

Research on E-Commerce Commodity Demand Forecasting and Inventory Optimization Based on Time Series Forecasting Model

In this study, 1996 sets of time series data are extracted from the three dimensions of merchant, warehouse and commodity, and the outliers are removed using the 3σ criterion. Through visual analysis, it was found that home improvement building materials and food and beverages were popular commodities. Then ARIMA and LSTM models were used to forecast the demand. Through 1-wmape index evaluation, LSTM model was found to perform better. Finally, the K-Means cluster analysis is carried out based on the categories of goods, merchants and warehouses, and the cluster number is selected as 70. The cosine similarity method is used to match the newly added dimensions to the category of the cluster in problem 1, and the ARIMA model is used for prediction. The results show that the cosine similarity method can accurately predict the commodity demand, the 1-wmape index is 0.801, and the residual plot presents randomness. The data of November 2022 is added to the original data, and the missing data is processed by linear interpolation. The LSTM model was used to forecast the increase of the total demand during Double 11. By setting a periodic time series with a step size of 30, the model achieves a good accuracy in predicting the demand in June, with a 1-wmape index of 0.829. To sum up, this study comprehensively considers three dimensions of merchants, warehouses and commodities, and uses different models to make accurate predictions. The ARIMA and LSTM models are adopted, while the promotion characteristics are considered and the appropriate LSTM parameters are selected. The research results provide practical forecasting tools and methods for supply chain management.

Evaluating the long term effectiveness of a Mediterranean marine protected area to tackle the effects of invasive and range expanding herbivorous fish on rocky reefs

Here we provide evidence, along an 8-year period time-series based on multifaceted data from a Mediterranean marine protected area (MPA), whether protection can tackle invasive and range expanding herbivore fishes, and their effects on the algal resource availability, taking into account the population trends of predatory fishes, fisheries catches of herbivore fishes and sea surface temperature (SST) through time. Our findings pointed out that an ineffective in restoring top-down control process MPA may facilitate, rather than alleviate, the sudden and enduring population burst of invasive and range-expanding herbivorous fishes at tipping points of abrupt change. This subsequently results in the deterioration of rocky reef habitats and the depletion of algal resources, with the tipping points of abrupt change for algal and herbivore fish species not overlapping chronologically. As sea temperature increases, ineffective or recently established MPAs may inadvertently facilitate the proliferation of invasive and range-expanding species, posing a significant challenge to management effectiveness and conservation objectives.

Investigating laypeople’s short- and long-term forecasts of COVID-19 infection cycles

How do laypeople anticipate the severity of the COVID-19 pandemic in the short and long term? The evolution of COVID-19 infection cases is characterized by wave-shaped cycles, and we examine how individuals make forecasts for this type of time series. Over 42 weeks, we ran forecasting experiments and elicited weekly judgments from the general public to analyze their forecasting behavior (Study 1). We find that laypeople often tend to dampen trends when generating judgmental forecasts, but the degree to which this happens depends on the evolution of the cyclic time series data. The observed forecasting behavior reveals evidence of an optimism bias in that people do not expect the number of infection cases to grow at the observed rate while believing that infection rates would drop at an even faster rate than they are. Also, our results suggest that laypeople’s forecasting judgments are affected by the magnitude of the present wave relative to the previously observed ones. Further, we provide evidence that laypeople rely on a cognitive heuristic for generating long-term forecasts. People tend to rely on a linear discounting rule in that they lower their long-term forecasts proportionally to the interval of the forecast horizon, i.e., from tomorrow to 6 months and from 6 months to 1 year. We also find that this linear discounting rule can change to an exponential one in reaction to externally generated optimistic information signals such as vaccine approval. Furthermore, we replicated the major findings of Study 1 in a more controlled setting with a hypothetical pandemic scenario and artificially generated time series (Study 2). Overall, the current research contributes to the judgmental forecasting literature and provides practical implications for decision-makers in the pandemic.

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.

Pengaruh Efektivitas Penerimaan Retribusi Pengujian Kendaraan Bermotor (PKB) dan Efektivitas Penerimaan Retribusi Terminal Terhadap Efektivitas Pendapatan Dinas Perhubungan Kota Bandung Periode 2019-2022

This research aims to determine the effect of the effectiveness of receiving motor vehicle testing retributions (PKB) and the effectiveness of receiving terminal retributions on the effectiveness of the Bandung City Transportation Department's revenue for the 2019-2022 period. The population in this research is the Bandung City Transportation Department budget realization report. The sample in this research is the budget realization report for the Bandung City Transportation Department for 48 months during the 2019-2022 period (time series). The method used in this research is a quantitative method with secondary data. The data analysis technique used is the classical assumption test, multiple linear regression analysis and then hypothesis testing using IBM SPSS Statistics 23. The research results show that partially the effectiveness of receiving PKB retributions (X1) has a negative effect on the income of the Bandung City Transportation Department, while the effectiveness of retributions receiving terminal (X2) does not have a positive effect on the income of the Bandung City Transportation Department. Simultaneously, the effectiveness of receiving PKB retributions and the effectiveness of receiving terminal retributions have a positive effect on the effectiveness of the Bandung City Transportation Department's revenue.

An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction

Traffic prediction is an important part of modern intelligent transportation systems (ITS), which helps transportation management and city planning. However, it is a very challenging task for modeling complex spatio-temporal dependencies, since the traffic data belongs to highly periodic multivariate time series which makes it hard to model accurate spatial dependencies only from time series and observed geolocation information of road segments. The existing research mainly focuses on finding ways of capturing dynamic spatial dependencies of road segments while neglecting the importance of periodicity, and few studies have explored a pure embedding-driven method that is robust to corrupted data to model periodicity. In this paper, we propose an embedding-driven multi-hop spatio-temporal attention network for traffic prediction (), which mainly focuses on leveraging the multi-scale periodicity of traffic data. Specifically, the proposed network applies a designed Fourier-series-based embedding, to capture the periodicity, which is more in line with real-world facts. Driven by the designed embedding, both local and global temporal dependencies are modeled properly by combining the attention-based methods and the convolution-based methods. Besides, we implement a trial that can hardly be seen in the existing traffic prediction works to combine the graph self-attention mechanism with a multi-hop diffusion process to explore the large-scale structural information on a designed set of graphs. Experiments on two real-world traffic datasets which contains traffic speed data for months show the effectiveness of our proposed methods. The experiments also suggest the methods can provide stable reasonable and smooth predictions for completely corrupted data.

Analysis of hybrid exploitation of wind and wave power in the Mediterranean and the Black Sea

The hybrid use of renewables enables resource complementation by other sources when one of them is inactive. This study focuses on wind and wave hybrids in the Mediterranean and the Black Sea with the aim of quantifying the potentials, directionality, correlation, and complementarity of the two sources. The investigation includes the spatial distributions and point-wise analysis capacity factors, the relationship between wave height and period, prevailing directions of wind and wave, bivariate probability analysis of wind and wave power, correlation of wind and wave power based on Pearson and Kendall’s Tau methods, and complementarity analysis by computing the active hours of one source when the other is inactive. The energy density, correlation, and complementarity analysis are realized considering the working conditions of a wind turbine with eight different wave energy converters by taking into account their power matrices. ERA5 dataset is used as a source for the wind and wave data in the basin with over six-decade long time series for each grid point. The results indicate that the wind turbine possesses higher capacity factors. For the wind turbine, the spatial maximum is 0.5, whereas for the wave energy converters, this value is 0.16 for the Pelamis device with the highest performance among the considered devices. The relationship between the wave climate parameters is investigated by fitting the wave height and period time series to an exponential function in all study area. Results are provided as spatial distributions of function coefficient which is hoped to be useful for the researchers when only one of the variables is known. Bivariate probabilities are computed for wind and wave power, and it is seen that for the selected analysis points, the wave power has a potential between 1 and 1.5 kW/m in the most probable axial locations. Directional analysis of wind and waves resulted in a map of the prevailing directions in the study area showing that the Northwest is the dominant direction. Correlation analysis between the wind and wave power time series of devices indicates that a positive correlation is evident in the study area up to + 0.69. The main result from the complementarity investigation accompanied by the correlation analysis showed that wind complementing wave condition is much more dominant in the region.

Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey’s Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a “bagging-like” ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).

Nonstationary time series change direction forecast method using improved leading indicator

AbstractThis paper presents a method for forecasting the change direction of nonstationary time series using the improved leading indicator. The leading indicator is a method developed by Ehlers that translates a time series mathematically in the future direction with respect to the time axis and calculates the leading value of the time series. However, this method has the problem that the leading value can be calculated only in the low frequency region with a normalized frequency of 0.06 or more (normalized period of 17 or more) at the maximum. In order to solve this problem, by the gentle slope the amplitude characteristics in the low frequency region of the leading indicator, it is possible to calculate the leading value in the frequency domain with a normalized frequency of 0.25 or more (normalized period of four or more) at the maximum. By applying the preceding value to the instantaneous periodic time series by the improved sine wave indicator developed by Ehlers, it is possible to forecast the change direction of the non‐stationary time series in the short term.

Investigating Deformation Mechanism of Earth-Rock Dams with InSaR and Numerical Simulation: Application to Liuduzhai Reservoir Dam, China

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

Search-and-rescue in the Central Mediterranean Route does not induce migration: Predictive modeling to answer causal queries in migration research

State- and private-led search-and-rescue are hypothesized to foster irregular migration (and thereby migrant fatalities) by altering the decision calculus associated with the journey. We here investigate this ‘pull factor’ claim by focusing on the Central Mediterranean route, the most frequented and deadly irregular migration route towards Europe during the past decade. Based on three intervention periods—(1) state-led Mare Nostrum, (2) private-led search-and-rescue, and (3) coordinated pushbacks by the Libyan Coast Guard—which correspond to substantial changes in laws, policies, and practices of search-and-rescue in the Mediterranean, we are able to test the ‘pull factor’ claim by employing an innovative machine learning method in combination with causal inference. We employ a Bayesian structural time-series model to estimate the effects of these three intervention periods on the migration flow as measured by crossing attempts (i.e., time-series aggregate counts of arrivals, pushbacks, and deaths), adjusting for various known drivers of irregular migration. We combine multiple sources of traditional and non-traditional data to build a synthetic, predicted counterfactual flow. Results show that our predictive modeling approach accurately captures the behavior of the target time-series during the various pre-intervention periods of interest. A comparison of the observed and predicted counterfactual time-series in the post-intervention periods suggest that pushback policies did affect the migration flow, but that the search-and-rescue periods did not yield a discernible difference between the observed and the predicted counterfactual number of crossing attempts. Hence we do not find support for search-and-rescue as a driver of irregular migration. In general, this modeling approach lends itself to forecasting migration flows with the goal of answering causal queries in migration research.

Decomposing the Mutual Information Rate of Heart Period and Respiration Variability Series to Assess Cardiorespiratory Interactions.

Heart rate variability results from the coupled activity of the cardiovascular and cardiorespiratory systems, which have their own internal regulation mechanisms but also interact with each other and with the autonomic nervous system to maintain homeostasis. In this work, the assessment of these physiological mechanisms is carried out decomposing the Mutual Information Rate (MIR), an information-theoretic measure of the interdependence between coupled processes, into terms of entropy rate or conditional mutual information related respectively to complexity and causality measures. These measures are computed using a non-parametric approach based on nearest-neighbors. The proposed framework is first tested on simulated autoregressive processes and then applied to experimental data consisting of heart period and respiratory time series measured in healthy subjects monitored at rest and during head-up tilt. Our results evidence that MIR decomposition is able to highlight the interdependence of short-term physiological mechanisms of cardiorespiratory interactions during postural stress.

ANALYSIS OF SOLVENCY VARIABLES IN MODERATING THE EFFECT OF MACROECONOMIC VARIABLES ON THIRD PARTY FUNDS (CASE STUDY ON BCA SYARIAH FOR THE 2013-2022 PERIOD)

This research was conducted to analyze solvency ability in moderating the effect of macroeconomic factors on third party funds at BCA Syariah for the 2013-2022 period. This type of research is quantitative research using MRA or Moderated Regression Analysis as an analytical tool. The data used in this study is secondary data with s monthly period time series pattern obtained from financial reports issued by BCA Syariah. This software for data analysis used in this study is Eviews 10. The results obtained are that inflation has no effect on third party funds, the BI Rate has no effect on third party funds, the money supply has a positive and significant effect on third party funds, gross domestic product has no influence on third party funds, solvency can moderate the relationship of currency exchange rates to third party funds, and solvency can moderated the relationship of the joint stock price index to third party funds.