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Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control,G. E. P. Box,G. M. Jenkins,G. Reinsel

Time Series Analysis: Forecasting and Control   (Citations: 1360)
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Published in 1994.
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    • ...Second, we used Dickey-Fuller (Dickey and Fuller 1979) and Box-Jenkins (Box et al. 1994) methods to detect and specify autocorrelation in the residuals of the equation estimated in step one...
    • ...Applying the Box and Jenkins method (Box et al. 1994) to the series indicated that taking their first differences would remove autocorrelation from them...

    Ralph A. Catalanoet al. Temperature oscillations may shorten male lifespan via natural selecti...

    • ...The differencing technique can be adapted to eliminate the seasonality of period d by introducing the lag-d difference operation [7]...
    • ...Seasonal ARIMA models [7] allow for randomness in the seasonal pattern from one cycle to the next...
    • ...The autocorrelation function of a stationary time series is defined by [7 ]a s...
    • ...The partial autocorrelation function of a stationary time series is defined by [7 ]a s...

    Zhe Wanget al. Spectrum Occupancy Statistics and Time Series Models for Cognitive Rad...

    • ...Many such prediction algorithms have already been proposed [25]...

    Vlad Naeet al. Dynamic Resource Provisioning in Massively Multiplayer Online Games

    • ...In contrast to the physics-based model, the ARX model [30] of order 9 fits the noisy measurement data...

    Evgeniya Bogatyrenkoet al. Efficient physics-based tracking of heart surface motion for beating h...

    • ...Furthermore, we apply the proposed weighted fuzzy interpolative reasoning method and the proposed GA-based weight-learning algorithm to deal with the truck backer-upper control problem, the computer activity prediction problem [37], the multivariate regression problems [15], and the time series prediction problems [3], [12], [27]...
    • ...In Section V, we apply the proposed method to deal with the truck backer-upper control problem [32], the computer activity prediction problem [37], the multivariate regression problems [15] and the time series prediction problems [3], [12], [27]...
    • ...In this section, we apply the proposed weighted fuzzy interpolative reasoning method and the proposed GA-based weightlearning algorithm to deal with the truck backer-upper control problem [39], the computer activity prediction problem [37], the multivariate regression problems [15] and the time series prediction problems [3], [12], [27]...
    • ... weighted fuzzy interpolative reasoning method and the proposed GA-based weight-learning algorithm to deal with the multivariate regression problems and the time series prediction problem, such as the abalone problem [15], the concrete compressive strength problem [15], the concrete slump test problem [15], the Mackey-Glass chaotic time series prediction problem [12], the chemical process concentration readings prediction problem [3], the ...
    • ... GA-based weight-learning algorithm to deal with the multivariate regression problems and the time series prediction problem, such as the abalone problem [15], the concrete compressive strength problem [15], the concrete slump test problem [15], the Mackey-Glass chaotic time series prediction problem [12], the chemical process concentration readings prediction problem [3], the chemical process temperature readings prediction problem [3], ...
    • ... deal with the multivariate regression problems and the time series prediction problem, such as the abalone problem [15], the concrete compressive strength problem [15], the concrete slump test problem [15], the Mackey-Glass chaotic time series prediction problem [12], the chemical process concentration readings prediction problem [3], the chemical process temperature readings prediction problem [3], the gas furnace prediction problem [3] ...
    • ...In this paper, we adopt the fivefold cross validation for the experiments of the multivariate regression problems [15] and the time series prediction problems [3], [12], [27], respectively...
    • ...strength problem [15], the concrete slump test problem [15], the Mackey-Glass chaotic time series prediction problem [12], the chemical process concentration readings prediction problem [3] the chemical process temperature readings prediction problem [3], the gas furnace prediction problem [3] and the wave force prediction problem [27]...
    • ...strength problem [15], the concrete slump test problem [15], the Mackey-Glass chaotic time series prediction problem [12], the chemical process concentration readings prediction problem [3] the chemical process temperature readings prediction problem [3], the gas furnace prediction problem [3] and the wave force prediction problem [27]...
    • ...strength problem [15], the concrete slump test problem [15], the Mackey-Glass chaotic time series prediction problem [12], the chemical process concentration readings prediction problem [3] the chemical process temperature readings prediction problem [3], the gas furnace prediction problem [3] and the wave force prediction problem [27]...
    • ...For example, based on the experimental results with respect to the testing samples of “0 fuzzification”, as shown in Tables III and V, we can get the average ranks of the proposed method and the methods presented in [1], [4], [10], [22] and [36] with respect to the nine problems [3], [12], [15], [27], [37], as shown in Table VI. Based on the Friedman test [33], we can calculate the Friedman statistic FF = 19.08 with 10 − 1 = 9 and (10 − 1) ...
    • ... fuzzy interpolative reasoning method by the use of the optimally learned weights that are obtained by the proposed GA-based weightlearning algorithm produces better accuracy than the methods presented in [1], [4], [10], [11], [22], [32] and [36] when dealing with the truck backer-upper control problem [32], the computer activity prediction problem [37], the multivariate regression problems [15] and the time series prediction problems [3], ...

    Shyi-Ming Chenet al. Weighted Fuzzy Rule Interpolation Based on GA-Based Weight-Learning Te...

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