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Journal articles on the topic 'Model time series analysis'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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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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Huang, Guangdong, and Jiahong Li. "Hybrid Time Series Method for Long-Time Temperature Series Analysis." Discrete Dynamics in Nature and Society 2021 (July 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/9968022.

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This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algorithms and the observed data as the labels and target of the XGBoost algorithm, respectively. Through the data training and testing of the XGBoost algorithm, the optimal weights and the corresponding output of the hybrid DAX model can be calculated. Root mean squared error (RMSE) was followed as the criteria for judging the precision. This paper compared DAX with an equal-weighted average (EWA) algorithm and 10 DA algorithms. The result shows that the RMSE of the two hybrid algorithms is much lower than that of the DA algorithms. Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well.

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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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Bratčikovienė, Nomeda. "Adapted SETAR model for lithuanian HCPI time series." Nonlinear Analysis: Modelling and Control 17, no. 1 (January 25, 2012): 27–46. http://dx.doi.org/10.15388/na.17.1.14076.

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We present adapted SETAR (self-exciting threshold autoregressive) model, which enables simultaneous estimation of nonlinearity and unobserved time series components. This model was tested on real Lithuanian harmonised consumer price index (HCPI) time series, covering the period from January 1996 to December 2009. The results show that adapted SETAR model is able to capture features of the real time series with complex nature. ARIMA model has also been used for the same time series for the comparison. Evaluated models and results of the comparison are presented in this work.

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Momani, P. E. Naill M. "Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis." American Journal of Environmental Sciences 5, no. 5 (May 1, 2009): 599–604. http://dx.doi.org/10.3844/ajessp.2009.599.604.

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6

TSAUR, RUEY-CHYN, HSIAO-FAN WANG, and JIA-CHI O.-YANG. "FUZZY REGRESSION FOR SEASONAL TIME SERIES ANALYSIS." International Journal of Information Technology & Decision Making 01, no. 01 (March 2002): 165–75. http://dx.doi.org/10.1142/s0219622002000117.

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Fuzzy regression model is an alternative to evaluate the relation between independent variables and dependent variable among the forecasting models when the data are not sufficient to identify the relation. Such phenomenon is significant especially for seasonal variation data for which large amount of data are required to show the pattern. However, few researches have been done on this issue. Because of its increasing importance in industries, in this study, we propose a method of applying fuzzy regression model for this purpose. By using two independent variables of preceding periodical data and index of time, the developed model not only shows the pattern of the seasonal variation, but also the yearly trend. From the results of the illustration, the average forecasting error is below 1.85% which, in comparison to the most commonly used Quadratic Trend Analysis of 2.91% and the Double Exponential Smoothing Model of 4.29%, has a better performance.

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7

Kim, Hyesuk, and Incheol Kim. "Human Activity Recognition as Time-Series Analysis." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/676090.

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We propose a system that can recognize daily human activities with a Kinect-style depth camera. Our system utilizes a set of view-invariant features and the hidden state conditional random field (HCRF) model to recognize human activities from the 3D body pose stream provided by MS Kinect API or OpenNI. Many high-level daily activities can be regarded as having a hierarchical structure where multiple subactivities are performed sequentially or iteratively. In order to model effectively these high-level daily activities, we utilized a multiclass HCRF model, which is a kind of probabilistic graphical models. In addition, in order to get view-invariant, but more informative features, we extract joint angles from the subject’s skeleton model and then perform the feature transformation to obtain three different types of features regarding motion, structure, and hand positions. Through various experiments using two different datasets, KAD-30 and CAD-60, the high performance of our system is verified.

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8

Novotny, V., H. Jones, X. Feng, and A. Capodaglio. "Time Series Analysis Models of Activated Sludge Plants." Water Science and Technology 23, no. 4-6 (February 1, 1991): 1107–16. http://dx.doi.org/10.2166/wst.1991.0562.

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Time series models of the activated sludge process are very useful in design and real time operation of wastewater treatment systems which deal with variable influent flows and pollution loads. In contrast to common deterministic dynamic mathematical models which require knowledge of a large number of coefficients, the time series models can be developed from input and output monitoring data series. In order to avoid “black box” approaches, time series models can be made compatible and identical in principle, with their dynamic mass balance model equivalents. In fact, these two types of models may differ only in nomenclature. ARMA-Transfer Function models can be used for systems which are linear or can be linearized such as typical BOD or suspended solids influent-effluent relationships for which the type of model is known. For systems which are highly nonlinear, and/or the input-output model is unknown, neural network models can be used. Both ARMA-TF models and neural network models can be made self-learning, that is, the performance of the model can be periodically improved manually or in an automated mode as new information is collected by monitoring. Application examples are included.

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9

Ince, Huseyin, and Fatma Sonmez Cakir. "Analysis of financial time series with model hybridization." Pressacademia 4, no. 3 (September 30, 2017): 331–41. http://dx.doi.org/10.17261/pressacademia.2017.700.

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10

Parzen, E. "Time Series Model Identification and Quantile Spectral Analysis." IFAC Proceedings Volumes 18, no. 5 (July 1985): 731–36. http://dx.doi.org/10.1016/s1474-6670(17)60647-5.

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11

Fukuchi, J.-I. "Subsampling and model selection in time series analysis." Biometrika 86, no. 3 (September 1, 1999): 591–604. http://dx.doi.org/10.1093/biomet/86.3.591.

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12

Agarwal, Anish, Muhammad Jehangir Amjad, Devavrat Shah, and Dennis Shen. "Model Agnostic Time Series Analysis via Matrix Estimation." ACM SIGMETRICS Performance Evaluation Review 47, no. 1 (December 17, 2019): 85–86. http://dx.doi.org/10.1145/3376930.3376984.

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13

Agarwal, Anish, Muhammad Jehangir Amjad, Devavrat Shah, and Dennis Shen. "Model Agnostic Time Series Analysis via Matrix Estimation." Proceedings of the ACM on Measurement and Analysis of Computing Systems 2, no. 3 (December 21, 2018): 1–39. http://dx.doi.org/10.1145/3287319.

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14

Tsaur, Ruey-Chyn, Jia-Chi O Yang, and Hsiao-Fan Wang. "Fuzzy relation analysis in fuzzy time series model." Computers & Mathematics with Applications 49, no. 4 (February 2005): 539–48. http://dx.doi.org/10.1016/j.camwa.2004.07.014.

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15

Foreman, M. G. G., and R. F. Henry. "The harmonic analysis of tidal model time series." Advances in Water Resources 12, no. 3 (September 1989): 109–20. http://dx.doi.org/10.1016/0309-1708(89)90017-1.

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16

Chen, Yin Ping, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, and Shu Xiu Feng. "Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model." Advanced Materials Research 709 (June 2013): 819–22. http://dx.doi.org/10.4028/www.scientific.net/amr.709.819.

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The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)12 was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and practical to apply the approach of ARIMA model in fitting time series to predict pulmonary tuberculosis within a short lead time.

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17

Cummins, Bree, Tomas Gedeon, Shaun Harker, and Konstantin Mischaikow. "Model Rejection and Parameter Reduction via Time Series." SIAM Journal on Applied Dynamical Systems 17, no. 2 (January 2018): 1589–616. http://dx.doi.org/10.1137/17m1134548.

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18

Ghosh, Himadri, G. Sunilkumar, and Prajneshu. "Mixture Nonlinear Time-Series Analysis : Modelling and Forecasting." Calcutta Statistical Association Bulletin 57, no. 1-2 (March 2005): 95–108. http://dx.doi.org/10.1177/0008068320050108.

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Gaussian mixture transition distribution (GMTD) models and mixture autoregressive (MAR) models are generally employed to describe those data sets that depict sudden bursts, outliers and flat stretches at irregular time epochs. In this paper , these two approaches are compared by considering weekly wholesale onion price data during April, 1998 to November, 2001. After eliminating trend, seasonal fluctuations are studied by fitting BoxJenkins airline model to residual series. To this end, null hypothesis of presence of nonseasonal and seasonal stochastic trends is tested by using OsboruChuiSmithBirchenhall (OCSB) auxiliary regression. Subsequently, appropriate filters in airline model for seasonal fluctuations are selected. Presence of autoregressive co nditional heteroscedasticity (ARCH) is tested by Naive Lagrange multiplier (Nave LM) test. Estimation of parameters is carric~d out using ExpectationMaximization (EM) algorithm and the best model is selected on the basis of Bayesian information criterion (BIC). Outofsample forecasting is performed for onestep and twostep ahead prediction by uaive approach, proposed by Wong and Li (2000). It is concluded that, for data under consideration, a threecomponent MAR model performs the best.

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19

Luo, Yu, and Yulin Wang. "A Statistical Time-Frequency Model for Non-stationary Time Series Analysis." IEEE Transactions on Signal Processing 68 (2020): 4757–72. http://dx.doi.org/10.1109/tsp.2020.3014607.

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20

Chaolong, Jia, Xu Weixiang, Wang Futian, and Wang Hanning. "Track Irregularity Time Series Analysis and Trend Forecasting." Discrete Dynamics in Nature and Society 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/387857.

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The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

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21

Guo, Shuangshuang, Linlin Tang, Xiaoyan Guo, and Zheng Huang. "Power Customer Complaint Prediction Model Based on Time Series Analysis." Revue d'Intelligence Artificielle 34, no. 4 (September 30, 2020): 471–77. http://dx.doi.org/10.18280/ria.340412.

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To improve customer service of power enterprises, this paper constructs an intelligent prediction model for customer complaints in the near future based on the big data on power service. Firstly, three customer complaint prediction models were established, separately based on autoregressive integrated moving average (ARIMA) time series algorithm, multiple linear regression (MLR) algorithm, and backpropagation neural network (BPNN) algorithm. The predicted values of the three models were compared with the real values. Through the comparison, the BPNN model was found to achieve the best predictive effect. To help the BPNN avoid local minimum, the genetic algorithm (GA) was introduced to optimize the BPNN model. Finally, several experiments were conducted to verify the effect of the optimized model. The results show that the relative error of the optimized model was less than 40% in most cases. The proposed model can greatly improve the customer service of power enterprises.

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22

Rafi, Muhammad, Mohammad Taha Wahab, Muhammad Bilal Khan, and Hani Raza. "Towards optimal ATM cash replenishment using time series analysis." Journal of Intelligent & Fuzzy Systems 41, no. 6 (December 16, 2021): 5915–27. http://dx.doi.org/10.3233/jifs-201953.

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Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

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23

Nazir, Hafiza Mamona, Ijaz Hussain, Muhammad Faisal, Alaa Mohamd Shoukry, Showkat Gani, and Ishfaq Ahmad. "Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis." Complexity 2019 (January 2, 2019): 1–14. http://dx.doi.org/10.1155/2019/2782715.

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Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single-stage model (without denoised and decomposed), the hybrid two-stage model (with denoised), and existing three-stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three-stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one-stage and two-stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.

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24

Zhang, Hao, Xi Shi, and Li Fang Lai. "Research on Time Series Analysis Based Deformation Prediction Model." Advanced Materials Research 250-253 (May 2011): 2888–91. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.2888.

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This paper introduces a method to apply time series analysis in dam deformation monitoring and prediction. We provide a simplified AR prediction model, which is relatively optimized in fitting constructive dynamic deformation features, analyzing deformation data and predicting deformation trend. We use this AR model in a certain dam’s deformation data processing, and prove it is an effective dynamic deformation prediction model.

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25

Yu, Zhi Tao. "Gold Investment Risk Analysis Model Based on Time Series." Advanced Materials Research 926-930 (May 2014): 3834–37. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3834.

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With the growing size of the gold market, all kinds of gold investment varieties are constantly emerging, namely, meet the residents needs and requirements of the investment risk, also makes the prime financial rise. This paper analyzes quantify the risk of gold market fundamentals, and has a deep research on the historical development of the global gold market, global gold market developing trends and factors affecting the gold price. This paper focuses on analysis of VAR risk management theory and VAR-GARCH model. VAR-GARCH model can be more effective on the VAR value forecast, which is a better way to estimate the gold market risk. In addition, VAR-GARCH conditional variance model is also analyzed, and high-risk the real market is the corresponding.

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26

Tsay, Ruey S. "Model Checking via Parametric Bootstraps in Time Series Analysis." Applied Statistics 41, no. 1 (1992): 1. http://dx.doi.org/10.2307/2347612.

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27

Sevilla, Diego J. R. "A Simple Pile-up Model for Time Series Analysis." Astrophysical Journal 843, no. 1 (June 29, 2017): 44. http://dx.doi.org/10.3847/1538-4357/aa72e8.

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28

Bogl, Markus, Wolfgang Aigner, Peter Filzmoser, Tim Lammarsch, Silvia Miksch, and Alexander Rind. "Visual Analytics for Model Selection in Time Series Analysis." IEEE Transactions on Visualization and Computer Graphics 19, no. 12 (December 2013): 2237–46. http://dx.doi.org/10.1109/tvcg.2013.222.

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29

TSAY, RUEY S., and GEORGE C. TIAO. "Use of canonical analysis in time series model identification." Biometrika 72, no. 2 (1985): 299–315. http://dx.doi.org/10.1093/biomet/72.2.299.

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30

Yang, Zhizhong, and Bao Xi. "Time Series Analysis Based on Improved Kalman Filter Model." International Journal of Multimedia and Ubiquitous Engineering 10, no. 7 (July 31, 2015): 183–90. http://dx.doi.org/10.14257/ijmue.2015.10.7.19.

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31

Zhou, Huiyu, and Kotaro Hirasawa. "Traffic conduction analysis model with time series rule mining." Expert Systems with Applications 41, no. 14 (October 2014): 6524–35. http://dx.doi.org/10.1016/j.eswa.2014.03.009.

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32

Bohara, A. K., and R. F. McNown. "Multiple time series analysis of simultaneous equations model specification." Empirical Economics 17, no. 3 (September 1992): 383–99. http://dx.doi.org/10.1007/bf01206300.

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33

Western, Bruce, and Meredith Kleykamp. "A Bayesian Change Point Model for Historical Time Series Analysis." Political Analysis 12, no. 4 (2004): 354–74. http://dx.doi.org/10.1093/pan/mph023.

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Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

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34

Enns, Peter K., Nathan J. Kelly, Takaaki Masaki, and Patrick C. Wohlfarth. "Moving forward with time series analysis." Research & Politics 4, no. 4 (October 2017): 205316801773223. http://dx.doi.org/10.1177/2053168017732231.

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In a recent Research and Politics article, we showed that for many types of time series data, concerns about spurious relationships can be overcome by following standard procedures associated with cointegration tests and the general error correction model (GECM). Matthew Lebo and Patrick Kraft (LK) incorrectly argue that our recommended approach will lead researchers to identify false (i.e., spurious) relationships. In this article, we show how LK’s response is incorrect or misleading in multiple ways. Most importantly, when we correct their simulations, their results reinforce our previous findings, highlighting the utility of the GECM when estimated and interpreted correctly.

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35

von Sachs, Rainer. "Nonparametric Spectral Analysis of Multivariate Time Series." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 361–86. http://dx.doi.org/10.1146/annurev-statistics-031219-041138.

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Spectral analysis of multivariate time series has been an active field of methodological and applied statistics for the past 50 years. Since the success of the fast Fourier transform algorithm, the analysis of serial auto- and cross-correlation in the frequency domain has helped us to understand the dynamics in many serially correlated data without necessarily needing to develop complex parametric models. In this work, we give a nonexhaustive review of the mostly recent nonparametric methods of spectral analysis of multivariate time series, with an emphasis on model-based approaches. We try to give insights into a variety of complimentary approaches for standard and less standard situations (such as nonstationary, replicated, or high-dimensional time series), discuss estimation aspects (such as smoothing over frequency), and include some examples stemming from life science applications (such as brain data).

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Zhao, Yue Ling, Hai Yan Han, Li Ying Cao, and Gui Fen Chen. "Study on Maize Yield Prediction Using Time Series Analysis." Advanced Materials Research 1049-1050 (October 2014): 1463–66. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1463.

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In order to predict the maize yield of changes in the following years for our managers and framers, we have applied a statistical model based on time series analysis. Nowadays, there is a variety of methods on yield prediction in agricultural .In order to prove the accuracy of prediction models, more than prediction model can be used. In the paper, the establishment of ARIMA (2, 1, 1) model was been established by using the timing sequence analysis on yield data. The partition results can not only guide farmer, provide an important method for accurately predicting in agricultural products, and can be used to implement variable input and precise fertilization recommendation.

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Gluhovsky, Alexander, and Ernest Agee. "On the Analysis of Atmospheric and Climatic Time Series." Journal of Applied Meteorology and Climatology 46, no. 7 (July 1, 2007): 1125–29. http://dx.doi.org/10.1175/jam2512.1.

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Abstract Linear parametric models are commonly assumed and used for unknown data-generating mechanisms. This study demonstrates the value of inferring statistics of meteorological and climatological time series by using a computer-intensive subsampling method that allows one to avoid time series analysis anchored in parametric models with imposed perceived physical assumptions. A first-order autoregressive model, typically adopted as the default model for correlated time series in climate studies, has been selected and altered with a nonlinear component to provide insight into possible errors in estimation due to nonlinearities in the real data-generating mechanism. The nonlinearity undetected by basic diagnostic procedures is shown to invalidate statistical inference based on the linear model, whereas the inference derived through subsampling remains valid. It is argued that subsampling and other resampling methods are preferable in complex dependent-data situations that are typical for atmospheric and climatic series when the real data-generating mechanism is unknown.

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Liang, Mengxia, Xiaolong Wang, and Shaocong Wu. "A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis." Entropy 23, no. 6 (June 8, 2021): 731. http://dx.doi.org/10.3390/e23060731.

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Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

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39

Bretó, Carles, Daihai He, Edward L. Ionides, and Aaron A. King. "Time series analysis via mechanistic models." Annals of Applied Statistics 3, no. 1 (March 2009): 319–48. http://dx.doi.org/10.1214/08-aoas201.

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40

Cavanaugh, Joseph. "Regression Models for Time Series Analysis." Journal of the American Statistical Association 99, no. 465 (March 2004): 299. http://dx.doi.org/10.1198/jasa.2004.s324.

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41

Ray, Bonnie K. "Regression Models for Time Series Analysis." Technometrics 45, no. 4 (November 2003): 364. http://dx.doi.org/10.1198/tech.2003.s166.

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42

Bhansali, Rajendra J. "Model specification and selection for multivariate time series." Journal of Multivariate Analysis 175 (January 2020): 104539. http://dx.doi.org/10.1016/j.jmva.2019.104539.

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43

Huang, D. "Stochastic fm models and non-linear time series analysis." Advances in Applied Probability 29, no. 4 (December 1997): 986–1003. http://dx.doi.org/10.2307/1427850.

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An important model in communications is the stochastic FM signal st = A cos , where the message process {mt} is a stochastic process. In this paper, we investigate the linear models and limit distributions of FM signals. Firstly, we show that this non-linear model in the frequency domain can be converted to an ARMA (2, q + 1) model in the time domain when {mt} is a Gaussian MA (q) sequence. The spectral density of {St} can then be solved easily for MA message processes. Also, an error bound is given for an ARMA approximation for more general message processes. Secondly, we show that {St} is asymptotically strictly stationary if {mt} is a Markov chain satisfying a certain condition on its transition kernel. Also, we find the limit distribution of st for some message processes {mt}. These results show that a joint method of probability theory, linear and non-linear time series analysis can yield fruitful results. They also have significance for FM modulation and demodulation in communications.

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Padhye, Nikhil S., and Sandra K. Hanneman. "Cosinor Analysis for Temperature Time Series Data of Long Duration." Biological Research For Nursing 9, no. 1 (July 2007): 30–41. http://dx.doi.org/10.1177/1099800407303509.

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The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

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Lan, Ma, and Ou Shangheng. "Characteristic Analysis of Flight Delayed Time Series." Journal of Intelligent Systems 30, no. 1 (December 1, 2020): 361–75. http://dx.doi.org/10.1515/jisys-2020-0045.

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Abstract In order to analyze the characteristics of airport flight delayed time series, based on the construction of flight delay time series, firstly, the K-means algorithm is used to cluster the time series of delayed departures. Secondly, combining with R/S analysis method of Fractal theory, Hurst index of the series is calculated, and Fractal characteristics of the series are analyzed. Then, the VAR (Vector Auto Regression) model is constructed, and Impulse Response Function (IRF) and Variance Decomposition are conducted to explore the impact of the fluctuation of flight delay time series on the future delay. The results show that K-means algorithm divides the time series into five categories, and each category has significant characteristics. Hurst index values of different time series are in the interval of (0.5, 1), indicating that the time series have good fractal characteristics. Through the IRF and Variance Decomposition of VAR model, results show that the time series are significantly affected by random pulses, and the prediction changes of the series come from multiple time series fluctuations. The prediction results show that the flight delay time series is predictable.

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Huang, D. "Stochastic fm models and non-linear time series analysis." Advances in Applied Probability 29, no. 04 (December 1997): 986–1003. http://dx.doi.org/10.1017/s0001867800047984.

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An important model in communications is the stochastic FM signal st = A cos , where the message process {m t} is a stochastic process. In this paper, we investigate the linear models and limit distributions of FM signals. Firstly, we show that this non-linear model in the frequency domain can be converted to an ARMA (2, q + 1) model in the time domain when {mt } is a Gaussian MA (q) sequence. The spectral density of {St } can then be solved easily for MA message processes. Also, an error bound is given for an ARMA approximation for more general message processes. Secondly, we show that {St } is asymptotically strictly stationary if {m t } is a Markov chain satisfying a certain condition on its transition kernel. Also, we find the limit distribution of st for some message processes {mt }. These results show that a joint method of probability theory, linear and non-linear time series analysis can yield fruitful results. They also have significance for FM modulation and demodulation in communications.

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47

Harris, David. "Principal Components Analysis of Cointegrated Time Series." Econometric Theory 13, no. 4 (February 1997): 529–57. http://dx.doi.org/10.1017/s0266466600005995.

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This paper considers the analysis of cointegrated time series using principal components methods. These methods have the advantage of requiring neither the normalization imposed by the triangular error correction model nor the specification of a finite-order vector autoregression. An asymptotically efficient estimator of the cointegrating vectors is given, along with tests forcointegration and tests of certain linear restrictions on the cointegrating vectors. An illustrative application is provided.

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Wesonga, Ronald, Fabian Nabugoomu, and Brian Masimbi. "Airline Delay Time Series Differentials." International Journal of Aviation Systems, Operations and Training 1, no. 2 (July 2014): 64–76. http://dx.doi.org/10.4018/ijasot.2014070105.

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Flight delays affect passenger travel satisfaction and increase airline costs. The authors explore airline differences with a focus on their delays based on autoregressive integrated moving averages. Aviation daily data were used in the analysis and model development. Time series modelling for six airlines was done to predict delays as a function of airport's timeliness performance. Findings show differences in the time series prediction models by airline. Differential analysis in the time series prediction models for airline delay suggests variations in airline efficiencies though at the same airport. The differences could be attributed to different management styles in the countries where the airlines originate. Thus, to improve airport timeliness performance, the study recommends airline disaggregated studies to explore the dynamics attributable to determinants of airline unique characteristics.

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Monge Sanz, B. M., and N. J. Medrano Marqués. "Total ozone time series analysis: a neural network model approach." Nonlinear Processes in Geophysics 11, no. 5/6 (December 16, 2004): 683–89. http://dx.doi.org/10.5194/npg-11-683-2004.

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Abstract. This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the considered European mid-latitudes. Artificial neural networks (ANNs) are intrinsically non-linear systems, hence they are expected to cope with TO series better than classical statistics do. Moreover, neural networks do not assume the stationarity of the data series so they are also able to follow time-changing situations among the implicated variables. These two features turn NNs into a promising tool to catch the interactions between atmospheric variables, and therefore to extract as much information as possible from the available data in order to make, for example, time series reconstructions or future predictions. Models based on NNs have also proved to be very suitable for the treatment of missing values within the data series. In this paper we present several models based on neural networks to fill the missing periods of data within a total ozone time series, and models able to reconstruct the data series. The results released by the ANNs have been compared with those obtained by using classical statistics methods, and better accuracy has been achieved with the non linear ANNs techniques. Different network structures and training strategies have been tested depending on the specific task to be accomplished.

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Iwueze, Iheanyi S., Anthony C. Akpanta, and Hycinth C. Iwu. "Seasonal Analysis of Transformations of the Multiplicative Time Series Model." Asian Journal of Mathematics & Statistics 1, no. 2 (April 15, 2008): 80–89. http://dx.doi.org/10.3923/ajms.2008.80.89.

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