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Journal articles on the topic 'Time series analysi'

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Published: 9 March 2023

Last updated: 10 March 2023

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1

Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (January 28, 2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.

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2

Tsujimoto, Kazuki, and Toshiaki Omori. "Switching Probabilistic Slow Feature Analysis for Time Series Data." International Journal of Machine Learning and Computing 10, no. 6 (December 2020): 740–45. http://dx.doi.org/10.18178/ijmlc.2020.10.6.999.

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3

Thinsungnoen, Tippaya, Kittisak Kerdprasop, and Nittaya Kerdprasop. "A Deep Learning of Time Series for Efficient Analysis." International Journal of Future Computer and Communication 6, no. 3 (September 2017): 123–27. http://dx.doi.org/10.18178/ijfcc.2017.6.3.503.

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4

Tu, Min, Yuanjian Zhang, Jianfeng Xu, and Yu Li. "Analysis and Modeling of Time Series Based on Granular Computing." International Journal of Future Computer and Communication 4, no. 2 (April 2015): 93–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.363.

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5

Delima, Allemar Jhone P. "Application of Time Series Analysis in Projecting Philippines’ Electric Consumption." International Journal of Machine Learning and Computing 9, no. 5 (October 2019): 694–99. http://dx.doi.org/10.18178/ijmlc.2019.9.5.860.

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6

Anupriya and Anita Singhrova. "Comparative Analysis of Time Series Forecasting Models for SDMN Traffic." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 531–40. http://dx.doi.org/10.5373/jardcs/v11/20192602.

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7

RAJASEKARAN, RAJKUMAR, GOVINDA K, ASHRITH REDDY, UDAY SAI REDDY, and YASHWANTH REDDY. "Visual Analysis of Temperature Time Series and Rainfall Using Big Data." Journal of Research on the Lepidoptera 50, no. 3 (September 25, 2019): 39–52. http://dx.doi.org/10.36872/lepi/v50i3/201023.

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8

Hanafiah, Mohd Hafiz Mohd, and Mohd Fauzi Mohd Harun. "Tourism Demand in Malaysia: A cross-sectional pool time-series analysis." International Journal of Trade, Economics and Finance 1, no. 1 (2010): 80–83. http://dx.doi.org/10.7763/ijtef.2010.v1.15.

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9

Hanafiah, Mohd Hafiz Mohd, and Mohd Fauzi Mohd Harun. "Tourism Demand in Malaysia: A cross-sectional pool time-series analysis." International Journal of Trade, Economics and Finance 1, no. 2 (2010): 200–203. http://dx.doi.org/10.7763/ijtef.2010.v1.36.

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10

Bunrit, Supaporn, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Multiresolution Analysis Based on Wavelet Transform for Commodity Prices Time Series Forecasting." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 175–80. http://dx.doi.org/10.18178/ijmlc.2018.8.2.683.

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11

Verma, Govind, A. Mehta, and S. S. Bhandari. "Trend analysis and Applicability of Fuzzy Time Series Models to Night-time Luminosity Data of Uttarakhand state, India." Indian Journal Of Science And Technology 15, no. 28 (July 28, 2022): 1372–80. http://dx.doi.org/10.17485/ijst/v15i28.764.

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12

Pandian, Dr A. Serma Saravana, Dr M. Prabu Dr. M. Prabu, and Dr G. Senthilkumar Dr. G. Senthilkumar. "Time Series Analysis of the Contribution of Livestock Sector to the National Economy." Indian Journal of Applied Research 3, no. 3 (October 1, 2011): 94–95. http://dx.doi.org/10.15373/2249555x/mar2013/30.

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13

Pushpalatha, E. "Incidence and Prevalence of Malaria in Thrissur District, Kerala: A Time Series Analysis." Journal of Communicable Diseases 50, no. 01 (March 29, 2018): 16–21. http://dx.doi.org/10.24321/0019.5138.201804.

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14

Yu, Jae Jin, Jang soo Kim, and Dong Ho JANG. "안면도 바람아래 할미섬 주변의 시계열적 침식·퇴적환경 변화 분석." JOURNAL OF THE GEOMORPHOLOGICAL ASSOCIATION OF KOREA 23, no. 1 (March 31, 2016): 47–60. http://dx.doi.org/10.16968/jkga.23.1.4.

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15

Dwivedi, Suneet. "Quantifying predictability of Indian summer monsoon intraseasonal oscillations using nonlinear time series analysis." Meteorologische Zeitschrift 21, no. 4 (August 1, 2012): 413–19. http://dx.doi.org/10.1127/0941-2948/2012/0350.

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16

kaur, Kirandeep. "Time Series Analysis of Indian External Debt, Total Reserves and Economic Growth Rates." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 105–7. http://dx.doi.org/10.15373/22778179/apr2014/37.

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17

Babirath, Julia, Karel Malec, Rainer Schmitl, Kamil Maitah, and Mansoor Maitah. "Forecasting based on spectral time series analysis: prediction of the Aurubis stock price." Investment Management and Financial Innovations 17, no. 4 (December 4, 2020): 215–27. http://dx.doi.org/10.21511/imfi.17(4).2020.20.

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The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

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18

Foster, Grant, and Patrick T. Brown. "Time and tide: analysis of sea level time series." Climate Dynamics 45, no. 1-2 (July 5, 2014): 291–308. http://dx.doi.org/10.1007/s00382-014-2224-3.

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19

Santos, A. M. P., M. F. Borges, N. Crato, H. Mendes, and B. Mota. "Sardine regime shifts off Portugal: a time series analysis of catches and wind conditions." Scientia Marina 67, S1 (April 30, 2003): 235–44. http://dx.doi.org/10.3989/scimar.2003.67s1235.

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20

Lin Shiyu, 林时雨, 闫雪娇 Yan Xuejiao, 谢哲 Xie Zhe, 付宏文 Fu Hongwen, 蒋松 Jiang Song, 姜宏志 Jiang Hongzhi, 李旭东 Li Xudong, and 赵慧洁 Zhao Huijie. "基于时间序列及邻域分析的管道点云障碍物检测." Laser & Optoelectronics Progress 59, no. 22 (2022): 2210007. http://dx.doi.org/10.3788/lop202259.2210007.

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21

Zempo, Y., and S. S. Kano. "Применение метода максимальной энтропии к временным рядам, получаемым в реальном времени в рамках нестационарной теории функционала плотности." Успехи кибернетики / Russian Journal of Cybernetics, no. 2 (June 30, 2021): 64–73. http://dx.doi.org/10.51790/2712-9942-2021-2-2-5.

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The maximum entropy method is one of the key techniques for spectral analysis. The main feature is to describe spectra in low frequency with short timeseries data. We adopted the maximum entropy method to analyze the spectrum from the dipole moment obtained by the timedependent density functional theory calculation in real time, which is intensively studied and applied to computing optical properties. In the maximum entropy method analysis, we proposed that we use the concatenated data set made from severaltimes repeated raw data together with the phase. We have applied this technique to spectral analysis of the dynamic dipole moment obtained from timedependent density functional theory dipole moment of several molecules such as oligofluorene with n = 8. As a result, the higher resolution can be obtained without any peak shift due to the phase jump. The peak position is in good agreement to that of FT with just raw data. This paper presents the efficiency and characteristic features of this technique. Метод максимальной энтропии — один из основных в спектральном анализе. Его главная особенность — описание низкочастотных спектров короткими временными рядами данных. Авторы применили метод максимальной энтропии для анализа спектров дипольного момента, полученных расчетами в реальном времени по нестационарной теории функционала плотности. Данный вопрос интенсивно изучается и находит практическое применение при расчетах оптических свойств. При анализе методом максимальной энтропии предложено использовать объединенные наборы данных, включающие несколько повторяющихся последовательностей исходных данных с учетом фазы. Данный метод был применен при проведении спектрального анализа динамического дипольного момента, рассчитанного по нестационарной теории функционала плотности на основе дипольного момента нескольких молекул — в частности, молекул олигофлуорена при n = 8. В итоге удалось повысить разрешение без смещения максимумов из-за скачка фазы. Положение максимумов хорошо согласуется с результатами применения преобразования Фурье к необработанным исходным данным. В настоящей статье представлены особенности данного метода и показатели его эффективности.

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22

Izuchukwu, Oji-Okoro, Huang Huiping, Abba Shehu Abubakar, and Edun Adetunji Olufemi. "FDI Trade and Its Effects on Agricultural Development in Nigeria: Evidence From Time Series Analysis." International Journal of Management Science and Business Administration 1, no. 1 (2014): 28–40. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.11.1003.

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Agricultural sector is seen as an engine that contributes to the growth of the overall economy of Nigeria, despite several government efforts the sector is still characterized with low yields and limited areas under cultivation due to government dependence on mono-agricultural economy based on oil. This study attempts to evaluate the impacts of FDI, trade and its effects on agricultural sector development in Nigeria between the periods of 1980-2009, in analyzing the variables (VAR) model was used employing a three-step procedure. The Unit root test was conducted using the Augmented Dickey Fuller (ADF) and Philips-Parron (PP). Johansen and Juselius multivariate Cointregration test indicate that there is a present of cointregration. Granger causality test result shows that the variables employed have a bidirectional relationship, unidirectional relationship and no casual relationship. It is recommended that in order to boost agricultural output and develop the sector as a whole, more FDI should not only be sourced, there is a need for the government to provide legal and administrative quality framework and encourage more exportation of agricultural output that will enhance foreign exchange earnings and improve the competitiveness of Nigeria agricultural produce in the international market.

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23

Schönwiese, Christian D., Tim Staeger, and Silke Trömel. "The hot summer 2003 in Germany. Some preliminary results of a statistical time series analysis." Meteorologische Zeitschrift 13, no. 4 (September 2, 2004): 323–27. http://dx.doi.org/10.1127/0941-2948/2004/0013-0323.

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24

Menzel, Annette, and Volker Dose. "Analysis of long-term time series of the beginning of flowering by Bayesian function estimation." Meteorologische Zeitschrift 14, no. 3 (July 12, 2005): 429–34. http://dx.doi.org/10.1127/0941-2948/2005/0040.

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25

Zhong Bin, 钟彬, 柳志云 Liu Zhiyun, 李向新 Li Xiangxin, and 吕加颖 LÜ Jiaying. "滑坡形变的升降轨时序干涉合成孔径雷达监测与分析." Laser & Optoelectronics Progress 59, no. 24 (2022): 2428002. http://dx.doi.org/10.3788/lop202259.2428002.

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26

Mandal, Unmesh, Anirban Chakarborty, Phulen Mahato, and Goutam Das. "LinVec: A Stacked Ensemble Machine Learning Architecture for Analysis and Forecasting of Time-Series Data." Indian Journal Of Science And Technology 16, no. 8 (February 27, 2023): 570–82. http://dx.doi.org/10.17485/ijst/v16i8.2197.

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27

Bowerman, Bruce, and Jonathan D. Cryer. "Time Series Analysis." Technometrics 29, no. 2 (May 1987): 240. http://dx.doi.org/10.2307/1269781.

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28

Donatelli, Richard E., Ji-Ae Park, Spencer M. Mathews, and Shin-Jae Lee. "Time series analysis." American Journal of Orthodontics and Dentofacial Orthopedics 161, no. 4 (April 2022): 605–8. http://dx.doi.org/10.1016/j.ajodo.2021.07.013.

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29

Potscher, Benedikt M., and James D. Hamilton. "Time Series Analysis." Journal of the American Statistical Association 91, no. 433 (March 1996): 439. http://dx.doi.org/10.2307/2291435.

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30

Bakouch, Hassan S. "Time Series Analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 1 (January 2009): 283. http://dx.doi.org/10.1111/j.1467-985x.2008.00571_4.x.

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31

Subba Rao, T. "Time Series Analysis." Journal of Time Series Analysis 31, no. 2 (March 2010): 139. http://dx.doi.org/10.1111/j.1467-9892.2009.00641.x.

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32

Breitung, Jorg, and James D. Hamilton. "Time Series Analysis." Contemporary Sociology 24, no. 2 (March 1995): 271. http://dx.doi.org/10.2307/2076916.

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33

Taylor, Diana. "Time-Series Analysis." Western Journal of Nursing Research 12, no. 2 (April 1990): 254–61. http://dx.doi.org/10.1177/019394599001200210.

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34

Mills, Terence C. "TIME SERIES ANALYSIS." Journal of Economic Surveys 9, no. 3 (September 1995): 325–28. http://dx.doi.org/10.1111/j.1467-6419.1995.tb00120.x.

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35

Dattalo, Patrick. "Time Series Analysis." Journal of Community Practice 5, no. 4 (September 30, 1998): 67–85. http://dx.doi.org/10.1300/j125v05n04_05.

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36

Bowermnn, Bruce. "Time Series Analysis." Technometrics 29, no. 2 (May 1987): 240. http://dx.doi.org/10.1080/00401706.1987.10488217.

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37

Booth, David E. "Time Series Analysis." Technometrics 39, no. 1 (February 1997): 102–3. http://dx.doi.org/10.1080/00401706.1997.10485448.

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38

Fildes, Robert. "Time series analysis." International Journal of Forecasting 2, no. 1 (January 1986): 117. http://dx.doi.org/10.1016/0169-2070(86)90037-3.

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39

Marquez, Jamie. "Time series analysis." International Journal of Forecasting 11, no. 3 (September 1995): 494–95. http://dx.doi.org/10.1016/0169-2070(95)90035-7.

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40

Gasser, T. "Time series analysis." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S221. http://dx.doi.org/10.1016/0013-4694(85)90839-9.

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41

Watson, Mark W., and Jonathan D. Cryer. "Time Series Analysis." Journal of the American Statistical Association 82, no. 400 (December 1987): 1195. http://dx.doi.org/10.2307/2289427.

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42

Militino, Ana F. "Time Series Analysis." Journal of Applied Statistics 36, no. 6 (June 2009): 699–700. http://dx.doi.org/10.1080/02664760802366809.

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43

LEDERMAN, REGINA P. "Time Series Analysis." MCN, The American Journal of Maternal/Child Nursing 18, no. 3 (May 1993): 177. http://dx.doi.org/10.1097/00005721-199305000-00013.

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44

Raftery, Adrian E. "Time series analysis." European Journal of Operational Research 20, no. 2 (May 1985): 127–37. http://dx.doi.org/10.1016/0377-2217(85)90052-9.

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45

Lalley, Steven P., and A. B. Nobel. "Denoising deterministic time series." Dynamics of Partial Differential Equations 3, no. 4 (2006): 259–79. http://dx.doi.org/10.4310/dpde.2006.v3.n4.a1.

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46

Kumar, Praveen. "Climatic Determinants of Japanese Encephalitis in Bihar State of India: A Time-Series Poisson Regression Analysis." Journal of Communicable Diseases 49, no. 04 (January 3, 2018): 13–18. http://dx.doi.org/10.24321/0019.5138.201729.

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47

Ikeda, Yuji, Nobuyuki Kawahara, and Eiji Tomita. "Time-Series A/F Analysis in a SI Engine by Micro-Local Chemiluminescence Technique(Measurement, Species)." Proceedings of the International symposium on diagnostics and modeling of combustion in internal combustion engines 2004.6 (2004): 455–62. http://dx.doi.org/10.1299/jmsesdm.2004.6.455.

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48

Rudenko, Oleg, Oleksandr Bezsonov, and Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron." Development Management 17, no. 1 (May 7, 2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.

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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.

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49

Huitema, Bradley E., Ron Van Houten, and Hana Manal. "Time-series intervention analysis of pedestrian countdown timer effects." Accident Analysis & Prevention 72 (November 2014): 23–31. http://dx.doi.org/10.1016/j.aap.2014.05.025.

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50

Kim, Shin-Jou. "Does Trade Openness Benefit FDI Inflows into India?: Time-series Analysis on Five Major Indian FDI Sectors." Korea International Trade Research Institute 14, no. 5 (October 31, 2018): 101–14. http://dx.doi.org/10.16980/jitc.14.5.201810.101.

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