Time Series Analysis Methods Research Articles

Real-time collision detection based on external torque mutation suppression and time series analysis

PurposeAs the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.Design/methodology/approachThe external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.FindingsCompared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.Originality/valueOwing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

A Study on Forecasting Sales of New Energy Vehicles in China Based on Time Series Analysis

Combining the methods of multiple linear regression and time series analysis, this paper forecasts the sales trend of China's new energy electric vehicles in the next 10 years by using the data of new energy electric vehicle sales from 2013-2022, and comprehensively analyzes the impact of multiple factors, such as policy, technology, economy, market, and environment, on China's new energy electric vehicle sales. The study selected 16 secondary indicators including government subsidies, vehicle sales, number of charging piles, GDP per capita, consumer satisfaction, carbon emissions, etc. for data analysis, highlighting the key role of technological factors in the development of the industry. The study concludes that China's new energy electric vehicle industry is expected to usher in a period of sustained growth as technology continues to advance and the market matures.

Integrating generative AI into financial market prediction for improved decision making

This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the cGAN model can effectively capture the complexity of financial market data, and the deviation between the prediction results and the actual market performance is minimal, showing a high degree of accuracy. Through investment return analysis, the application value of model predictions in actual investment strategies is confirmed, providing investors with new ways to improve the decision-making process. In addition, the evaluation of model stability and reliability also shows that although there are still challenges in responding to market emergencies, overall, GAI technology has shown great potential and application value in the field of financial market prediction. The conclusion points out that integrating generative artificial intelligence into financial market forecasts can not only improve the accuracy of forecasts, but also provide powerful data support for financial decisions, helping investors make more informed decisions in a complex and ever-changing market environment. choose.

The Impact of Blockchain Technology on Financial Markets and Its Future Trends: An Economic Perspective Based on Data Analysis

Based on the economic perspective of data analysis, this paper deeply discusses the impact of blockchain technology on the financial market and its future trend. By using econometrics, we systematically analyze how blockchain technology can change the operation mechanism of financial markets, improve transaction efficiency, reduce transaction costs, and enhance the security and transparency of financial markets. The study has found that the decentralized nature of blockchain technology can help to break down the barriers of the traditional financial market and promote the popularization and inclusiveness of financial services. At the same time, the automatic execution function of smart contracts simplifies the process of financial transactions and reduces the risk of human intervention and fraud. In addition, the blockchain technology has also enhanced the trust mechanism of the financial markets by improving the security and traceability of the data. In terms of econometric analysis, we use a large amount of financial data to empirically test the impact of blockchain technology on the performance of the financial market by building regression models, time series analysis and other methods. The results show that there is a significant positive correlation between the application of blockchain technology and financial market efficiency, liquidity and stability. Looking into the future, with the continuous innovation and improvement of blockchain technology, its application in the financial market will be more extensive and deeper. On the one hand, blockchain technology is expected to promote the further digitalization and intelligence of the financial market, and improve the convenience and personalization of financial services. On the other hand, blockchain technology will also promote the cross-border integration and innovation of the financial market, and inject new vitality into the global economic development.

Impact of capitation on physicians’ behavior among patients with hypertension: an interrupted time series study in rural China

ObjectiveThe purpose of this study is to explore the change in physicians’ hypertension treatment behavior before and after the reform of the capitation in county medical community.MethodsSpanning from January 2014 to December 2019, monthly data of outpatient and inpatient were gathered before and after the implementation of the reform in April 2015. We employed interrupted time series analysis method to scrutinize the instantaneous level and slope changes in the indicators associated with physicians’ behavior.ResultsSeveral indicators related to physicians’ behavior demonstrated enhancement. After the reform, medical cost per visit for inpatient exhibited a reverse trajectory (-53.545, 95%CI: -78.620 to -28.470, p < 0.01). The rate of change in outpatient drug combination decelerated (0.320, 95%CI: 0.149 to 0.491, p < 0.01). The ratio of infusion declined for both outpatient and inpatient cases (-0.107, 95%CI: -0.209 to -0.004, p < 0.1; -0.843, 95%CI: -1.154 to -0.532, p < 0.01). However, the results revealed that overall medical cost per visit and drug proportion for outpatient care continued their initial upward trend. After the reform, the decline of drug proportion for outpatient care was less pronounced compared to the period prior to the reform, and length of stay also had a similar trend.ConclusionTo some extent, capitation under the county medical community encourages physicians to control the cost and adopt a more standardized diagnosis and treatment behavior. This study provides evidence to consider the impact of policy changes on physicians’ behavior when designing payment methods and healthcare systems aimed at promoting PHC.

EU regulations on reporting sustainable development as a determinant of limiting the environmental pressure of chemical enterprises in Poland

The period of validity of Directive 2014/95/EU, which imposed on enterprises the obligation to present non-financial information in separate non-financial reports, the so-called ESG was aimed to check its effects, complementary regulations and actual actions in the environmental area. Numerous works have analysed various aspects of non-financial reporting in the context of the directive, including several on ESG reporting in relation to air pollution emissions. However, it has not been examined yet whether there is a relationship between the level of reporting and the effects of pro-environmental activities in the context of the impact of regulations on it. A research gap in this area was identified, and therefore, the aim of the paper is to examine how selected chemical companies in Poland adapt to the EU guidelines on ESG reporting in the environmental dimension and whether it has an impact on reducing their pressure on the environment. The research period is 2010-2022, and the three-stage study used a quantitative-qualitative methodological approach. In the first and third stages, descriptive statistics and time series analysis methods were used. The second one includes methods such as content analysis, case analysis, GES Risk Rating, and expert analysis. The research carried out made it possible to conclude on the impact of non-financial reporting regulations on the environmental pressure.

Ensemble learning for landslide displacement prediction: A perspective of Bayesian optimization and comparison of different time series analysis methods

Ensemble learning for landslide displacement prediction: A perspective of Bayesian optimization and comparison of different time series analysis methods

Application of Artificial Neural Network Methods to Anatolian Plate Earthquake Magnitude and Location Prediction

Same-region earthquakes usually have a pattern that is difficult to identify clearly. Therefore, time series analysis methods have been proposed for earthquake prediction. Our work attempts to predict three earthquake parameters in the Anatolian Peninsula using pure artificial neural network methods. An optimized BP-NN model and optimally hyper-parameterized LSTM Model have been designed to predict earthquake magnitude, latitude, and longitude. The two models are compared among each other and with previous works for their prediction performances, using four well-accepted metrics: Mean Squared Error, Mean Absolute Error, Median Absolute Error, and Standard Deviation. The time, depth, sun, and moon distances to Earth were identified as the most contributing factors in earthquake occurrence through analysis by five different feature extraction algorithms. The date harmed the prediction accuracy. The LSTM model outperformed the BP-NN Model in magnitude prediction with 0.062 MSE. Latitude predictions of both methods were satisfactory and close. However, BP-NN had lower error rates in latitude prediction. However, longitude prediction errors were large in both models. Therefore, our designs were not successful in predicting the exact location of the earthquakes. However, multi-variate, stacked LSTM models are promising in predicting Anatolian Peninsula earthquake magnitudes, but future work is necessary for location and timing predictions.

Identifying the optimal operating regime of production wells based on the analysis of wellhead pressure fluctuations

The article presents the results of field experimental studies of wellhead and bottomhole pressure fluctuations at different operating modes of gas-liquid lift. As a result of the research, it was found that at the optimal operating mode of gas lift well there is minimum amplitude and maximum frequency of wellhead and bottomhole pressure fluctuations, which agrees well with the results of previously, conducted theoretical and experimental laboratory studies. It is shown that the increase in the amplitude and decrease in the frequency of wellhead and bottomhole pressure fluctuations is observed when gas injection deviates from the optimal one, both in the direction of increasing and decreasing gas injection. The possibility of assessing the optimal operating mode of gas-lift and flowing wells on the basis of analyzing the fluctuations of technological parameters during normal operation without special testing operations has been established. A methodology for assessing the efficiency of gas-lift and flowing wells operation mode based on the application of various methods of time series analysis was created. Keywords: gas lift well; gas-liquid flow; optimal rate; wellhead pressure; fluctuation.

COVID-19 Pandemıc Effect On House Price Index In Turkey: An Interrupted Time Series Analysis

The COVID-19 pandemic has had a significant impact on various aspects of everyday life,and the housing market is one of these factors.The purpose of this study is to investigate the effects of the COVID-19 pandemic on the housing market.&#x0D; In this context, the study analyzed the House price index(HPI) data for Turkey between 2018-2022 using the interrupted time series analysis(ITS) method to examine the effects of COVID-19 on the housing market. &#x0D; According to the analysis, there has been a decrease in the HPI immediately after COVID-19 and this decrease is statistically significant(-105.726 and p=0.003). However, it has been found that the trend of this decrease has changed and also, it has been determined that the housing price index has increased by 14.521 points every month and year(p=0.001). &#x0D; As a result,this study, which shows statistically significant effects, has revealed that COVID-19 pandemic has caused significant changes in housing prices throughout Turkey.

Power Demand Forecasting Using ANN and Prophet Models for the Load Despatch Center in Andhra Pradesh, India

This paper uses various data variables to develop and analyze ANN and Prophet models for power demand forecasting in Andhra Pradesh, India. The electricity power consumption in Andhra Pradesh was about 51,756.000 GWh in 2021. Currently, there is a great emphasis on saving power. Power Demand Forecasting is creating much interest, and many models, such as artificial neural networks combined with other techniques based on real-life phenomena, are used and tested. These models have become an essential part of the power and energy sector. This paper considered specific time-series analysis methods and deep-learning techniques for short-term power demand forecasting. This paper also analyzes and compares results between the prophet and ANN models to predict power demand in Andhra Pradesh, India. Our results comparatively revealed the model's appropriateness for the problem. Both models performed well in three performance metrics: accuracy, generalization, and robustness. However, the AI model exhibits better accuracy than Prophet for the historical data set. The time taken for model fitting is also comparatively less for the AI models. The forecast accuracy of the electricity was in the range of 95 to 97.65.

A Study on Replenishment Decision of Vegetable Goods Based on Polynomial Regression and Arima Time Series Forecasting

In a fresh food superstore, the superstore replenishes daily based on the historical sales and demand of vegetable commodities. To formulate the optimal replenishment decision, this paper analyses the sales flow, purchase cost, and loss of a supermarket in a period, and through the use of a variety of models, it can give a reference opinion on the formulation of automatic pricing and replenishment decisions for vegetable commodities. The first text describes the process of analyzing the relationship between total sales volume and cost-plus pricing, including data integration, preprocessing, and multiple regression analysis methods. Through these analyses, a general relationship between total sales volume and merchandise selling price is identified. Univariate linear regression, polynomial regression, random forest regression, and BP neural network regression models were all used to analyze this relationship. The focus is then on the total daily replenishment and pricing strategy for the coming week to maximize the superstore's revenue. The text details the use of ARIMA time series analysis methods, as well as steps for noise reduction and forecasting using Wavelet wavelet functions. Through these methods, the text provides sales forecast results for different vegetable categories for the coming week for the superstore management's reference.

Financial assets prediction based on ARIMA, Random Forest and GRU

As a matter of fact, financial asset prediction is a domain of great interest because of its potential to generate revenue. In reality, financial asset prediction models have evolved from basic time series analysis models to contemporarily hybrid models with the help of machine learning algorithms. To be specific, this study will introduce and analyse three popular financial asset forecasting models and their hybrids in terms of the properties they have demonstrated in the completed studies. In reality, good results have been achieved using a time series analysis method named autoregressive integrated moving average (ARIMA) for capturing linear elements. According to the analysis, in dealing with data noise and interpretability, Random Forest (RF) algorithm, a machine learning technique, produced positive outcomes. Deep learning technique gate recurrent unit (GRU) produced positive outcomes in terms of prediction accuracy. Based on the evaluations, this study indicates future research directions in the field of financial asset forecasting by analysing and organizing the characteristics of three different mainstream models.

A small-sample time-series signal augmentation and analysis method for quantitative assessment of bradykinesia in Parkinson's disease

Patients with Parkinson's disease (PD) usually have varying degrees of bradykinesia, and the current clinical assessment is mainly based on the Movement Disorder Society Unified PD Rating Scale, which can hardly meet the needs of objectivity and accuracy. Therefore, this paper proposed a small-sample time series classification method (DTW-TapNet) based on dynamic time warping (DTW) data augmentation and attentional prototype network. Firstly, for the problem of small sample sizes of clinical data, a DTW-based data merge method is used to achieve data augmentation. Then, the time series are dimensionally reorganized using random grouping, and convolutional operations are performed to learn features from multivariate time series. Further, attention mechanism and prototype learning are introduced to optimize the distance of the class prototype to which each time series belongs to train a low-dimensional feature representation of the time series, thus reducing the dependency on data volume. Clinical experiments were conducted to collect motion capture data of upper and lower limb movements from 36 patients with PD and eight healthy controls. For the upper limb movement data, the proposed method improved the classification accuracy, weighted precision, and kappa coefficient by 8.89%-15.56%, 9.22%-16.37%, and 0.13-0.23, respectively, compared with support vector machines, long short-term memory, and convolutional prototype network. For the lower limb movement data, the proposed method improved the classification accuracy, weighted precision, and kappa coefficient by 8.16%-20.41%, 10.01%-23.73%, and 0.12-0.28, respectively. The experiments and results show that the proposed method can objectively and accurately assess upper and lower limb bradykinesia in PD.

Progress in research of rash and fever syndrome surveillance and early warning

Objective: To introduce the progress in research of rash and fever syndrome (RFS) surveillance and early warning both at home and abroad, and provide reference for surveillance and prevention of RFS in China. Methods: The keywords "fever" "rash" and "surveillance" and others were used for a literature retrieval by using China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, PubMed and Web of Science. The languages of literatures were limited in Chinese and English. The key information of the literatures were collected and analyzed with Excel. Results: A total of 36 study papers (21 in Chinese and 15 in English) were included. The studies mainly focused on the pathogen surveillance of RFS (n=19). The pathogens included measles virus, varicella-zoster virus, rubella virus, enterovirus, human B19 virus, dengue virus, streptococcus group A, Salmonella typhi and Salmonella paratyphoid,human herpesvirus, mumps virus and adenovirus. Eight studies were about the surveillance in major events, such as sport game, World Expo and religious gathering, or sudden natural disasters, such as earthquake and tropical storm, during 2010-2015. Eight studies focused on case or epidemic surveillance, most of which were studies from other counties. The surveillance sites were medical institutions. RFS was diagnosed according to the International Classification of Diseases, 9th (ICD-9) and symptoms descripted in chief-complaint. Only one study in Mongolia conducted RFS epidemic prediction. The analysis methods of 36 papers included simple descriptive analysis, time-based early warning models (such as regression analysis, fixed threshold method, Hugh Hart control chart method and cumulative sum control chart method) and time series analysis method. Conclusions: In the future, RFS surveillance system should cover both known pathogens and emerging pathogens. Automatic surveillance using information capture and intelligent modelling can be applied to improve the sensitivity and specificity of RFS surveillance and early warning.

Implementing lee's model to apply fuzzy time series in forecasting bitcoin price

Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.

Implementing lee's model to apply fuzzy time series in forecasting bitcoin price

Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.

Graph signal reconstruction based on spatio-temporal features learning

This paper presents a new algorithm for reconstructing time-varying graph signals using spatiotemporal feature learning. We introduce a time series analysis method to capture the temporal stationarity of graph signals and propose a reconstruction model based on spatiotemporal coupling features. However, prior knowledge of the temporal stationarity of graph signals is required for this task. To address the issue, we analyze the spatio-temporal coupling feature in a graph-spectral view and design a learning framework to capture only the temporal feature based on the sampled signals. We reconstruct the graph signals using the proposed reconstruction model and feature-learning framework by solving an unconstrained optimization problem consisting of data fidelity and two regularization terms. Numerical results using both synthetic and real-world datasets demonstrate the superiority of the proposed reconstruction algorithm over existing methods.

Application of wavelet transformation and singular spectral analysis in time series decomposition

When studying a time series, it is traditionally customary to use a generalized mathematical model where a trend component, a cyclic component that describes the repeatability of a cyclic process over time intervals and a random component or noise – this component is responsible for factors hidden from the observer. It is assumed that the main trend and the cyclical component can be accurately described because they are formed by known factors that can be taken into account within the framework of models. The discrete wavelet transform is traditionally used to decompose BP into additive components. The first discrete wavelet transform was invented by the Hungarian mathematician Alfred Haar (1910-1920). In parallel with this method, in the 70s and 80s, the idea of a time series analysis method called SSA (Singular Spectrum Analysis) arose in Russia, the method was called "Caterpillar". During the existence of the method, it has received an extension of its application – automation of the allocation of trend and cyclic components of BP has been developed. Using the example of a study of the real time series of TRAFFAT, two methods of decomposition time series into additive components are considered. The complexity of determining the initial parameters is compared, the applicability to the decomposition of real time series is tested, and the quality of decomposition by both methods of one reference time series is compared. An assessment of the quality of the decomposition of the time series into components was made, as well as a comparison of the two methods in terms of the complexity of the analyst's application and the possibility of automating the selection of parameters.

Long and short-term power supply and demand forecasting based on time series analysis under high proportion clean energy integration

This paper focuses on the prediction of power supply and demand in the electric power system under a high proportion of clean energy integration using time series analysis. Firstly, the impact of clean energy integration on power supply and demand is analyzed, taking into account factors such as the volatility and seasonality of renewable energy sources. A case study is conducted in Region A, which comprises four areas in China, to forecast the power supply and demand in the electric power system. In terms of power demand, we employ two commonly used methods in time series analysis, namely SARIMAX and factor decomposition, to establish a comprehensive forecasting model. These methods are applied from both short-term and long-term perspectives to analyze the monthly maximum electricity demand of users, aiming to accurately predict power demand under a high proportion of clean energy integration. As for power supply, we utilize the least squares method to regressively fit the installed capacity of energy sources and analyze future trends. Through the predictions of both demand and supply, the stable operation of the power system is ensured.