Time Series Analysis Methods Research Articles

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.
 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. 
 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). 
 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.

Identifying Disconnected Agents in Multiagent Systems via External Estimators.

This article addresses the problem of identifying disconnected agents in multiagent systems via external estimators. Specifically, we employ external estimators with an appropriately designed decision rule to identify the disconnectedness (i.e., the status of being disconnected) between two arbitrarily chosen agents in formation-control multiagent systems. The design of the decision rule is inspired by the unit-root testing problem of autoregressive time series. To make the best possible decision, a best-effort procedure is also proposed. Then, by introducing the concept of connected components (or just components) in graph theory, and using the methods of consensus analysis and time-series analysis, we develop an analytical framework to show the theoretical performance of the designed decision rule. A particularly important result shown by our analysis is that the miss probability of the decision rule can converge to 0 as the number of data samples increases. Finally, simulation results validate the performance of the decision rule and the best-effort procedure, showing that they can perform well even in small samples.

A survey on Bayesian nonparametric learning for time series analysis

Time series analysis aims to understand underlying patterns and relationships in data to inform decision-making. As time series data are becoming more widely available across a variety of academic disciplines, time series analysis has become a rapidly growing field. In particular, Bayesian nonparametric (BNP) methods are gaining traction for their power and flexibility in modeling, predicting, and extracting meaningful information from time series data. The utility of BNP methods lies in their ability to encode prior information and represent complex patterns in the data without imposing strong assumptions about the underlying distribution or functional form. BNP methods for time series analysis can be applied to a breadth of problems, including anomaly detection, noise density estimation, and time series clustering. This work presents a comprehensive survey of the existing literature on BNP methods for time series analysis. Various temporal BNP models are discussed along with notable applications and possible approaches for inference. This work also highlights current research trends in the field and potential avenues for further development and exploration.

Forecasting Ro-Ro Freight Transportation Demand at Samsun Port: A Hybrid Method Approach

ABSTRACT Türkiye's extensive coastline and geopolitics position necessitates the importance of Ro-Ro transportation with neighbouring countries. Türkiye's rapidly growing Ro-Ro transportation significantly contributes to imports and exports, which is of great importance to the national economy. Samsun Port is one of the most active ports in Türkiye's Ro-Ro transportation sector, operating in the Black Sea region. This study examined Ro-Ro transportation at Samsun Port, and future cargo forecasting was conducted. For this purpose, artificial neural networks and time series analysis methods were combined. Input variables used in the study included the number of Ro-Ro ships arriving at the port between 2009 and 2021, population figures, a specialized CPI indicator (fresh fruits and vegetables), and export values. The output variable was the amount of cargo carried by RoRo ships. According to the results obtained, it was observed that Samsun Port would have sufficient capacity for Ro-Ro transportation in the next 27 months in terms of wharf, port area, and operational space. Keywords: Samsun Port, Ro-Ro transportation, Port capacity, Forecasting

Political Ideology Direction of Policy Agendas and Maternal Mortality Outcomes in the U.S., 1915–2007

ObjectivesThe causes for persistently high and increasing maternal mortality rates in the United States have been elusive.MethodsWe use the shift in the ideological direction of the Republican and the Democratic parties in the 1960s, to test the hypothesis that fluctuations in overall and race-specific maternal mortality rates (MMR) follow the power shifts between the parties before and after the Political Realignment (PR) of the 1960s.ResultsUsing time-series data analysis methods, we find that, net of trend, overall and race-specific MMRs were higher under Democratic administrations than Republican ones before the PR (1915–1965)—i.e., when the Democratic Party was a protector of the Jim Crow system. This pattern, however, changed after the PR (1966–2007), with Republican administrations underperforming Democratic ones—i.e., during the period when the Republican Party shifted toward a more economically and socially conservative agenda. The pre-post PR partisan shifts in MMRs were larger for Black (9.5%, p<.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<.01$$\\end{document}) relative to White mothers (7.4%, p<.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<.05$$\\end{document}) during the study period.Conclusions for PracticeThese findings imply that parties and the ideological direction of their agendas substantively affect the social determinants of maternal health and produce politized health outcomes.

Взаимосвязь финансового развития и экономического роста России (2000–2022 годы)

The purpose of the study is to establish a dynamic relationship between financial development, measured by the share of liquid liabilities to GDP, and the economic growth of the Russian Federation, measured as GDP per capita, for the period 2000-2022. Based on correlation analysis using the method of wavelet analysis, which allows us to identify empirical patterns in the relationship between variables on different time scales that cannot be detected traditional methods of time series analysis. The results of the study indicate that financial development in general led to economic growth in Russia in 2000-2022 and demonstrated the positive impact of financial development on economic growth, which is observed both in the short, medium and long term, alternating with the negative impact of financial development on economic activity in the short and medium term. This negative effect of financial development on the real sector of the country's economy is typical not only for Russia, but is especially relevant for low- and lower-middle-income countries. It was revealed that short- and medium-term negative effects later turn into positive effects in the long term, which is consistent, in particular, with the endogenous theory of growth. It is concluded that in the practical implementation of the liberalization policy aimed at financial development through financial deepening, it is necessary and important to distinguish between short-term, medium-term and long-term effects of financial development on economic development, given that the positive benefits obtained through financial deepening and liberalization are expected only if the economy is at a certain level the level of economic development, due, for example, to the formation of mature institutions.

Impact of Significant Discounts for Low-Income Groups of Passengers in Railway Transport on the Development of Transport Performances

To achieve the sustainable and green mobility available to everyone regardless of their social situation, it is necessary to regulate the fares by the responsible public authority. Some countries have introduced free fare or significant discounts for selected groups of passengers, such as children, students, and pensioners, which has brought up a question if such a measure is efficient from the social point of view. On one side, this approach would change the transport behaviors of socially disadvantaged groups of population, on the other side, it can lead to an outflow of fully paying passengers due to a lower comfort and quality of services. In this paper, we evaluate the impact of introduced measures in fares on the transport performances of rail passenger transport, using the methods of time series analysis and forecasting.