Search results for: RR interval time series

Authors: Reza Bazargan lari, Mohammad H. Fattahi

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Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for pre-processing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based pre-processing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.

Keywords: wavelet, de-noising, predictability, time series fractal analysis, valid length, ANN

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Aswathi Thrivikraman, S. Advaith

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The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model.

Keywords: LSTM, autoencoder, forecasting, seq2seq model

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

Procedia PDF Downloads 159

Authors: Syed Talib Abbas Raza, Tahseen Ahmed Jilani, Saleem Abdullah

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This paper presents interval-valued intuitionistic fuzzy soft sets based TOPSIS method for group decision making. The interval-valued intuitionistic fuzzy soft set is a mutation of an interval-valued intuitionistic fuzzy set and soft set. In group decision making problems IVIFSS makes the process much more algebraically elegant. We have used weighted arithmetic averaging operator for aggregating the information and define a new Hybrid Score Function as metric tool for comparison between interval-valued intuitionistic fuzzy values. In an illustrative example we have applied the developed method to a criminological problem. We have developed a group decision making model for integrating the imprecise and hesitant evaluations of multiple law enforcement agencies working on target killing cases in the country.

Keywords: group decision making, interval-valued intuitionistic fuzzy soft set, TOPSIS, score function, criminology

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Authors: K. A. Ramesh

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Purpose: The study is to find out the effect of isolated and combined aerobic and anaerobic interval training on stride length and stride frequency of Soccer players. Method(s): To achieve this purpose, 45 women Soccer players who participated in the Anna University, Tamilnadu, India. Intercollegiate Tournament was selected as subjects and were randomly divided into three equal groups of fifteen each, such as an anaerobic interval training group (group-I), anaerobic interval training group (group-II) and combined aerobic-anaerobic interval training group (group-III). The training program was conducted three days per weeks for a period of six weeks. Stride length and Stride frequency was selected as dependent variables. All the subjects of the three groups were tested on selected criterion variables at prior to and immediately after the training program. The concepts of dependent test were employed to find out the significant improvement due to the influence of training programs on all the selected criterion variables. The analysis of covariance (ANCOVA) was also used to analyze the significant difference, if, any among the experimental groups. Result(s): The result of the study revealed that combined group was higher than aerobic interval training and anaerobic interval training groups. Conclusion(s): It was concluded that when experimental groups were compared with each other, the combined aerobic – anaerobic interval training program was found to be greater than the aerobic and the anaerobic interval training programs on the development of stride length and stride frequency. High intensity, combined aerobic – anaerobic interval training program can be carried out in a more soccer specific way than plain running.

Keywords: stride length, stride frequency, interval training, soccer

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Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

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Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

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Authors: Pui Shan Wong, Kosuke Tashiro, Satoru Kuhara, Sachiyo Aburatani

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Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords: Escherichia coli, gene regulation, network, time-series

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: Andres F. Ramirez, Carlos F. Valencia

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The increasing interest in renewable energies strategies application and the path for diminishing the use of carbon related energy sources have encouraged the development of novel strategies for integration of solar energy into the electricity network. A correct inclusion of the fluctuating energy output of a photovoltaic (PV) energy system into an electric grid requires improvements in the forecasting and simulation methodologies for solar energy potential, and the understanding not only of the mean value of the series but the associated underlying stochastic process. We present a methodology for synthetic generation of solar irradiance (shortwave flux) and air temperature bivariate time series based on copula functions to represent the cross-dependence and temporal structure of the data. We explore the advantages of using this nonlinear time series method over traditional approaches that use a transformation of the data to normal distributions as an intermediate step. The use of copulas gives flexibility to represent the serial variability of the real data on the simulation and allows having more control on the desired properties of the data. We use discrete zero mass density distributions to assess the nature of solar irradiance, alongside vector generalized linear models for the bivariate time series time dependent distributions. We found that the copula autoregressive methodology used, including the zero mass characteristics of the solar irradiance time series, generates a significant improvement over state of the art strategies. These results will help to better understand the fluctuating nature of solar energy forecasting, the underlying stochastic process, and quantify the potential of a photovoltaic (PV) energy generating system integration into a country electricity network. Experimental analysis and real data application substantiate the usage and convenience of the proposed methodology to forecast solar irradiance time series and solar energy across northern hemisphere, southern hemisphere, and equatorial zones.

Keywords: copula autoregressive, solar irradiance forecasting, solar energy forecasting, time series generation

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Authors: Sooyoung Cho, In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

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Series hybrid UAV(Unmanned aerial vehicle) that is proposed in this paper performs VTOL(Vertical take-off and landing) using the battery and generator, and it applies the series hybrid system with combination of the small engine and generator when cruising flight. This system can be described as the next-generation system that can dramatically increase the UAV flight times. Also, UAV systems require a large energy at the time of VTOL to be conducted for a short time. Therefore, this paper designs PMSG(Permanent Magnet Synchronous Generator) having a high specific power considering VTOL through the FEA.

Keywords: PMSG, VTOL, UAV, high specific power density

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Authors: Victoria Siriaki Jorry, I. S. Mbalawata, Hayong Shin

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The main objective in a change detection problem is to develop algorithms for efficient detection of gradual and/or abrupt changes in the parameter distribution of a process or time series data. In this paper, we present a modified cumulative (MCUSUM) algorithm to detect the start and end of a time-varying linear drift in mean value of a time series data based on likelihood ratio test procedure. The design, implementation and performance of the proposed algorithm for a linear drift detection is evaluated and compared to the existing CUSUM algorithm using different performance measures. An approach to accurately approximate the threshold of the MCUSUM is also provided. Performance of the MCUSUM for gradual change-point detection is compared to that of standard cumulative sum (CUSUM) control chart designed for abrupt shift detection using Monte Carlo Simulations. In terms of the expected time for detection, the MCUSUM procedure is found to have a better performance than a standard CUSUM chart for detection of the gradual change in mean. The algorithm is then applied and tested to a randomly generated time series data with a gradual linear trend in mean to demonstrate its usefulness.

Keywords: average run length, CUSUM control chart, gradual change detection, likelihood ratio test

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Authors: Chetsada Noknoi

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The acceptance of Y series refers to the willingness and enjoyment of watching Y series without feeling different from general series. This occurs when people watch Y series and derive happiness and entertainment from it. The viewing experience has the most significant impact on Y series acceptance. This research aims to 1) investigate the levels of acceptance of sexual diversity, image of Y series Actors, media exposure, and Y series acceptance among the residents in three southern border provinces of Thailand, and 2) examine how acceptance of sexual diversity, actor perceptions in Y series, and media exposure influence Y series acceptance in these provinces. The sample consisted of 322 participants from the three southern border provinces of Thailand. The research instrument used was a questionnaire, and data were analyzed using frequency, percentage, mean, standard deviation, and multiple regression analysis. The findings revealed that overall, acceptance of sexual diversity, Image of Y series Actors, and Y series acceptance among the residents in three southern border provinces of Thailand were at a high level, while media exposure was moderate overall. However, the two factors that had the most significant impact on Y series acceptance in these provinces, ranked from highest to lowest influence, were media exposure and acceptance of sexual diversity. Both of these factors had a positive effect on Y series acceptance among the residents in three southern border provinces of Thailand. Collectively, these factors accounted for 40.7% of the variance in Y series acceptance among the residents in three southern border provinces of Thailand.

Keywords: acceptance, acceptance of sexual diversity, image of Y series actors, media exposure, Y series

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Authors: Anne-Loes K. Warps, Rob A. E. M. Tollenaar, Pieter J. Tanis, Jan Willem T. Dekker

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Anastomotic leakage after colorectal cancer surgery remains a severe complication. Early diagnosis and treatment are essential to prevent further adverse outcomes. In the literature, it has been suggested that earlier reintervention is associated with better survival, but anastomotic leakage can occur with a highly variable time interval to index surgery. This study aims to evaluate the time-interval between rectal cancer resection with primary anastomosis creation and reoperation, in relation to short-term outcomes, stratified for the use of a defunctioning stoma. Methods: Data of all primary rectal cancer patients that underwent elective resection with primary anastomosis during 2013-2019 were extracted from the Dutch ColoRectal Audit. Analyses were stratified for defunctioning stoma. Anastomotic leakage was defined as a defect of the intestinal wall or abscess at the site of the colorectal anastomosis for which a reintervention was required within 30 days. Primary outcomes were new stoma construction, mortality, ICU admission, prolonged hospital stay and readmission. The association between time to reoperation and outcome was evaluated in three ways: Per 2 days, before versus on or after postoperative day 5 and during primary versus readmission. Results: In total 10,772 rectal cancer patients underwent resection with primary anastomosis. A defunctioning stoma was made in 46.6% of patients. These patients had a lower anastomotic leakage rate (8.2% vs. 11.6%, p < 0.001) and less often underwent a reoperation (45.3% vs. 88.7%, p < 0.001). Early reoperations (< 5 days) had the highest complication and mortality rate. Thereafter the distribution of adverse outcomes was more spread over the 30-day postoperative period for patients with a defunctioning stoma. Median time-interval from primary resection to reoperation for defunctioning stoma patients was 7 days (IQR 4-14) versus 5 days (IQR 3-13 days) for no-defunctioning stoma patients. The mortality rate after primary resection and reoperation were comparable (resp. for defunctioning vs. no-defunctioning stoma 1.0% vs. 0.7%, P=0.106 and 5.0% vs. 2.3%, P=0.107). Conclusion: This study demonstrated that early reinterventions after anastomotic leakage are associated with worse outcomes (i.e. mortality). Maybe the combination of a physiological dip in the cellular immune response and release of cytokines following surgery, as well as a release of endotoxins caused by the bacteremia originating from the leakage, leads to a more profound sepsis. Another explanation might be that early leaks are not contained to the pelvis, leading to a more profound sepsis requiring early reoperations. Leakage with or without defunctioning stoma resulted in a different type of reinterventions and time-interval between surgery and reoperation.

Keywords: rectal cancer surgery, defunctioning stoma, anastomotic leakage, time-interval to reoperation

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Authors: S. Phanyaem

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This paper presents the confidence intervals for the effect size base on bootstrap resampling method. The meta-analytic confidence interval for effect size is proposed that are easy to compute. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence intervals with the existing confidence intervals. The best confidence interval method will have a coverage probability close to 0.95. Simulation results have shown that our proposed confidence intervals perform well in terms of coverage probability and expected length.

Keywords: effect size, confidence interval, bootstrap method, resampling

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Authors: Mrinal Jana, Geetanjali Panda

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In this paper a methodology is developed to solve a nonlinear fractional programming problem in which the coefficients of the objective function and constraints are interval parameters. This model is transformed into a general optimization problem and relation between the original problem and the transformed problem is established. Finally the proposed methodology is illustrated through a numerical example.

Keywords: fractional programming, interval valued function, interval inequalities, partial order relation

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Authors: Messis A., Adjebli A., Ayeche R., Talbi M., Tighilet K., Louardiane M.

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Cancers are the second cause of death worldwide. Prevalence and incidence of cancers is getting increased by aging and population growth. This study aims to predict and modeling the evolution of breast, Colorectal, Lung, Bladder and Prostate cancers over the period of 2014-2019. In this study, data were analyzed using time series analysis with double exponential smoothing method to forecast the future pattern. To describe and fit the appropriate models, Minitab statistical software version 17 was used. Between 2014 and 2019, the overall trend in the raw number of new cancer cases registered has been increasing over time; the change in observations over time has been increasing. Our forecast model is validated since we have good prediction for the period 2020 and data not available for 2021 and 2022. Time series analysis showed that the double exponential smoothing is an efficient tool to model the future data on the raw number of new cancer cases.

Keywords: cancer, time series, prediction, double exponential smoothing

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Authors: Siriporn Meenanan

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The objective of this study is to investigate the attitude of Thai students towards the Thai series "Hormones the Series Season 2". This study was conducted in the quantitative research, and the questionnaires were used to collect data from 400 people of the sample group. Descriptive statistics were used in data analysis. The findings reveal that most participants have positive comments regarding the series. They strongly agreed that the series reflects on the way of life and problems of teenagers in Thailand. Hence, the participants believe that if adults have a chance to watch the series, they will have the better understanding of the teenagers. In addition, the participants also agreed that the contents of the play are appropriate and satisfiable as the contents of “Hormones the Series Season 2” will raise awareness among the teens and use it as a guide to prevent problems that might happen during their teenage life.

Keywords: content analysis, attitude, Thai series, hormones the Series

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Authors: Asif Zaman, Moinul Islam Zaber, Amin Ahsan Ali

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In recent years of industrialization, the South Asian countries are being affected by air pollution due to a severe increase in fine particulate matter 2.5 (PM₂.₅). Among them, Bangladesh is one of the most polluting countries. In this paper, statistical analyses were conducted on the time series of PM₂.₅ from various districts in Bangladesh, mostly around Dhaka city. Research has been conducted on the dynamic interactions and relationships between PM₂.₅ concentrations in different zones. The study is conducted toward understanding the characteristics of PM₂.₅, such as spatial-temporal characterization, correlation of other contributors behind air pollution such as human activities, driving factors and environmental casualties. Clustering on the data gave an insight on the districts groups based on their AQI frequency as representative districts. Seasonality analysis on hourly and monthly frequency found higher concentration of fine particles in nighttime and winter season, respectively. Cross correlation analysis discovered a phenomenon of correlations among cities based on time-lagged series of air particle readings and visualization framework is developed for observing interaction in PM₂.₅ concentrations between cities. Significant time-lagged correlations were discovered between the PM₂.₅ time series in different city groups throughout the country by cross correlation analysis. Additionally, seasonal heatmaps depict that the pooled series correlations are less significant in warmer months, and among cities of greater geographic distance as well as time lag magnitude and direction of the best shifted correlated particulate matter time series among districts change seasonally. The geographic map visualization demonstrates spatial behaviour of air pollution among districts around Dhaka city and the significant effect of wind direction as the vital actor on correlated shifted time series. The visualization framework has multipurpose usage from gathering insight of general and seasonal air quality of Bangladesh to determining the pathway of regional transportation of air pollution.

Keywords: air quality, particles, cross correlation, seasonality

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Authors: Sule Sahin, Basak Bulut Karageyik

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This paper introduces a hazard rate function for the time of ruin to calculate the conditional probability of ruin for very small intervals. We call this function the force of ruin (FoR). We obtain the expected time of ruin and conditional expected time of ruin from the exact finite time ruin probability with exponential claim amounts. Then we introduce the FoR which gives the conditional probability of ruin and the condition is that ruin has not occurred at time t. We analyse the behavior of the FoR function for different initial surpluses over a specific time interval. We also obtain FoR under the excess of loss reinsurance arrangement and examine the effect of reinsurance on the FoR.

Keywords: conditional time of ruin, finite time ruin probability, force of ruin, reinsurance

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

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Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: Tamas Garamhegyi, Jozsef Kovacs, Rita Pongracz, Peter Tanos, Balazs Trasy, Norbert Magyar, Istvan G. Hatvani

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Like most environmental processes, shallow groundwater fluctuation under natural circumstances also behaves periodically. With the statistical tools at hand, it can easily be determined if a period exists in the data or not. Thus, the question may be raised: Does the estimated average period time characterize the whole time period, or not? This is especially important in the case of such complex phenomena as shallow groundwater fluctuation, driven by numerous factors. Because of the continuous changes in the oscillating components of shallow groundwater time series, the most appropriate method should be used to investigate its periodicity, this is wavelet spectrum analysis. The aims of the research were to investigate the periodic behavior of the shallow groundwater time series of an agriculturally important and drought sensitive region in Central-Eastern Europe and its relationship to the European pressure action centers. During the research ~216 shallow groundwater observation wells located in the eastern part of the Great Hungarian Plain with a temporal coverage of 50 years were scanned for periodicity. By taking the full-time interval as 100%, the presence of any period could be determined in percentages. With the complex hydrogeological/meteorological model developed in this study, non-periodic time intervals were found in the shallow groundwater levels. On the local scale, this phenomenon linked to drought conditions, and on a regional scale linked to the maxima of the regional air pressures in the Gulf of Genoa. The study documented an important link between shallow groundwater levels and climate variables/indices facilitating the necessary adaptation strategies on national and/or regional scales, which have to take into account the predictions of drought-related climatic conditions.

Keywords: climate change, drought, groundwater periodicity, wavelet spectrum and coherence analyses

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Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Ahmad Moussavi

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In this paper, we are concerned with the Jacobson semisimple skew inverse Laurent series rings R((x−1; α, δ)) and the skew Laurent power series rings R[[x, x−1; α]], where R is an associative ring equipped with an automorphism α and an α-derivation δ. Examples to illustrate and delimit the theory are provided.

Keywords: skew polynomial rings, Laurent series, skew inverse Laurent series rings

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Authors: S. Khoshemehry, M. J. Pourvaghar

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Inflammatory markers are known as the main predictors of cardiovascular diseases. This study aimed at determining the effect of 8 weeks of interval endurance training on hs-CRP level and some anthropometric parameters in overweight men. Following the call for participation in research project in Kashan, 73 volunteers participated in it and constituted the statistical population of the study. Then, 28 overweight young men from the age of 22 to 25 years old were randomly assigned into two groups of experimental and control group (n=14). Anthropometric and the blood sample was collected before and after the termination of the program for measuring hs-CRP. The interval endurance program was performed at 60 to 75% of maximum heart rate in 2 sessions per week for 8 weeks. Kolmogorov-Smirnov test was used to test whether two samples come from the same distribution and T-test was used to assess the difference of two groups which were statistically significant at the level of 0.05. The result indicated that there was a significant difference between the hs-RP, weight, BMI and W/H ratio of overweight men in posttest in the exercise group (P≤0.05) but not in the control group. Interval endurance training program causes decrease in hs-CRP level and anthropometric parameters.

Keywords: interval endurance training program, HS-CRP, overweight, anthropometric

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Authors: Ramin Vafadary, Maryam Khanbaghi

Abstract:

Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

Abstract:

As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

Procedia PDF Downloads 76