• Title/Summary/Keyword: time series

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JOINT ASYMPTOTIC DISTRIBUTIONS OF SAMPLE AUTOCORRELATIONS FOR TIME SERIES OF MARTINGALE DIFFERENCES

  • Hwang, S.Y.;Baek, J.S.;Lim, K.E.
    • Journal of the Korean Statistical Society
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    • v.35 no.4
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    • pp.453-458
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    • 2006
  • It is well known fact for the iid data that the limiting standard errors of sample autocorrelations are all unity for all time lags and they are asymptotically independent for different lags (Brockwell and Davis, 1991). It is also usual practice in time series modeling that this fact continues to be valid for white noise series which is a sequence of uncorrelated random variables. This paper contradicts this usual practice for white noise. We consider a sequence of martingale differences which belongs to white noise time series and derive exact joint asymptotic distributions of sample autocorrelations. Some implications of the result are illustrated for conditionally heteroscedastic time series.

Time Series Models for Performance Evaluation of Network Traffic Forecasting (시계열 모형을 이용한 통신망 트래픽 예측 기법연구)

  • Kim, S.
    • The Korean Journal of Applied Statistics
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    • v.20 no.2
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    • pp.219-227
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    • 2007
  • The time series models have been used to analyze and predict the network traffic. In this paper, we compare the performance of the time series models for prediction of network traffic. The feasibility study showed that a class of nonlinear time series models can be outperformed than the linear time series models to predict the network traffic.

Dimension Analysis of Chaotic Time Series Using Self Generating Neuro Fuzzy Model

  • Katayama, Ryu;Kuwata, Kaihei;Kajitani, Yuji;Watanabe, Masahide;Nishida, Yukiteru
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.857-860
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    • 1993
  • In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

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Stock Forecasting Using Prophet vs. LSTM Model Applying Time-Series Prediction

  • Alshara, Mohammed Ali
    • International Journal of Computer Science & Network Security
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    • v.22 no.2
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    • pp.185-192
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    • 2022
  • Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

Pattern Extraction of Manufacturing Time Series Data Using Matrix Profile (매트릭스 프로파일을 이용한 제조 시계열 데이터 패턴 추출)

  • Kim, Tae-hyun;Jin, Kyo-hong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.210-212
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    • 2022
  • In the manufacturing industry, various sensors are attached to monitor the status of production facility. In many cases, the data obtained through these sensors is time series data. In order to determine whether the status of the production facility is abnormal, the process of extracting patterns from time series data must be preceded. Also various methods for extracting patterns from time series data are studied. In this paper, we use matrix profile algorithm to extract patterns from the collected multivariate time series data. Through this, the pattern of multi sensor data currently being collected from the CNC machine is extracted.

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Correlation Analyses of the Temperature Time Series Data from the Heat Box for Energy Modeling in the Automobile Drying Process (자동차 건조 공정 에너지 예측 모형을 위한 공조기 온도 시계열 데이터의 상관관계 분석)

  • Lee, Chang-Yong;Song, Gensoo;Kim, Jinho
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.37 no.2
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    • pp.27-34
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    • 2014
  • In this paper, we investigate the statistical correlation of the time series for temperature measured at the heat box in the automobile drying process. We show, in terms of the sample variance, that a significant non-linear correlation exists in the time series that consist of absolute temperature changes. To investigate further the non-linear correlation, we utilize the volatility, an important concept in the financial market, and induce volatility time series from absolute temperature changes. We analyze the time series of volatilities in terms of the de-trended fluctuation analysis (DFA), a method especially suitable for testing the long-range correlation of non-stationary data, from the correlation perspective. We uncover that the volatility exhibits a long-range correlation regardless of the window size. We also analyze the cross correlation between two (inlet and outlet) volatility time series to characterize any correlation between the two, and disclose the dependence of the correlation strength on the time lag. These results can contribute as important factors to the modeling of forecasting and management of the heat box's temperature.

A Study on the Test and Visualization of Change in Structures Associated with the Occurrence of Non-Stationary of Long-Term Time Series Data Based on Unit Root Test (Unit Root Test를 기반으로 한 장기 시계열 데이터의 Non-Stationary 발생에 따른 구조 변화 검정 및 시각화 연구)

  • Yoo, Jaeseong;Choo, Jaegul
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.7
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    • pp.289-302
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    • 2019
  • Structural change of time series means that the distribution of observations is relatively stable in the period of constituting the entire time series data, but shows a sudden change of the distribution characteristic at a specific time point. Within a non-stationary long-term time series, it is important to determine in a timely manner whether the change in short-term trends is transient or structurally changed. This is because it is necessary to always detect the change of the time series trend and to take appropriate measures to cope with the change. In this paper, we propose a method for decision makers to easily grasp the structural changes of time series by visualizing the test results based on the unit root test. Particularly, it is possible to grasp the short-term structural changes even in the long-term time series through the method of dividing the time series and testing it.

A Study on Fuzzy Time Series Prediction Method using the Genetic Algorithm (유전자 알고리즘을 이용한 퍼지 시계열예측 방법에 관한 연구)

  • Jee, Hyun-Min;Chang, Woo-Seok;Lee, Sung-Mok;Kang, Hwan-Il
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.622-624
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    • 2005
  • This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

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Visualization Tool of Distortion-Free Time-Series Matching (왜곡 제거 시계열 매칭의 시각화 도구)

  • Moon, Seongwoo;Lee, Sanghun;Kim, Bum-Soo;Moon, Yang-Sae
    • KIPS Transactions on Software and Data Engineering
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    • v.4 no.9
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    • pp.377-384
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    • 2015
  • In this paper we propose a visualization tool for distortion-free time-series matching. Supporting distortion-free is a very important factor in time-series matching to get more accurate matching results. In this paper, we visualize the result of time-series matching, which removes various time-series distortions such as noise, offset translation, amplitude scaling, and linear trend by using moving average, normalization, linear detrending transformations, respectively. The proposed visualization tool works as a client-server model. The client sends a user-selected time-series, of which distortions are removed, to the server and visualizes the matching results. The server efficiently performs the distortion-free time-series matching on the multi-dimensional R*-tree index. By visualizing the matching result as five different charts, we can more easily and more intuitively understand the matching result.

Time Series Perturbation Modeling Algorithm : Combination of Genetic Programming and Quantum Mechanical Perturbation Theory (시계열 섭동 모델링 알고리즘 : 운전자 프로그래밍과 양자역학 섭동이론의 통합)

  • Lee, Geum-Yong
    • The KIPS Transactions:PartB
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    • v.9B no.3
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    • pp.277-286
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    • 2002
  • Genetic programming (GP) has been combined with quantum mechanical perturbation theory to make a new algorithm to construct mathematical models and perform predictions for chaotic time series from real world. Procedural similarities between time series modeling and perturbation theory to solve quantum mechanical wave equations are discussed, and the exemplary GP approach for implementing them is proposed. The approach is based on multiple populations and uses orthogonal functions for GP function set. GP is applied to original time series to get the first mathematical model. Numerical values of the model are subtracted from the original time series data to form a residual time series which is again subject to GP modeling procedure. The process is repeated until predetermined terminating conditions are met. The algorithm has been successfully applied to construct highly effective mathematical models for many real world chaotic time series. Comparisons with other methodologies and topics for further study are also introduced.