time series process modeling
Sugarcane transportation process modeling by time series approach
... of time when the tractor operators spend queuing is still very ...waiting time for each tractor may take up to 20 hours to complete the handling process, which cause significant increases in ...
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Advances in Statistical Network Modeling and Nonlinear Time Series Modeling
... stationary process, the number of knots should be of the order of O(T ζ ) for available sample size T and some positive con- stant ζ to achieve a satisfying rate of convergence of the estimated nonlinear function ...
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Feature Selection for Time Series Modeling
... The correlation methods mentioned above only consider correlation from features to response. It tends to select redundant features if these features are all highly related to response but they are mutually correlated ...
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Time series behavior modeling with digital twin for Internet of Vehicles
... in time and space, described as event start time, deadline, dwell time, queuing time, service time, leaving time, pa- tronage frequency, service type, location, accommodat- ing ...
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TIME SERIES MODELING OF TROPICAL RIVER RUNOFF
... The process of estimating the future stages of flood flows and its time sequence at selected vulnerable points along the river course is called “Real Time Flood Forecasting” (Brath, 1999), is an ...
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Time series modeling and forecasting of the consumer price index in Belgium
... the series in order to achieve stationarity. Once this process is over, the researcher will then examine the correlogram in order to decide on the appropriate orders of the AR and MA ...same process; ...
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Detection and predictive modeling of chaos in finite hydrological time series
... is mixed with the same underlying chaotic signal. Several properties are observed. First, the saturation value of the cor- relation exponent increases as the white noise component (σ ) increases. Second, a threshold ...
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Identification and Modeling of Outliers in a Discrete - Time Stochastic Series
... the modeling of outliers in discrete-stochastic series are to identify the locations and types of outliers and estimating the effects of ...the series has multiple outliers that occur in patches, ...
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Autoregressive nonlinear time-series modeling of traffic fatalities in Europe
... of time series of road safety among different countries has been an interesting research ...underlying process that relates motorization levels with personal risk and can prove to be beneficial for ...
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Time series modeling for syndromic surveillance
... We built models of ED utilization using a time-series an- alytic approach. Models were constructed through an iter- ative process and were trained on roughly the first eight years of data (2,775 ...
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Factor modeling for high dimensional time series
... Pan & Yao (2008) who identified the factor loading space by expanding the white noise space step by step and used portmanteau tests as a stopping rule. Although our estimation procedure is based upon the same ...
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Time Series Modeling and Forecasting of CPI of Bangladesh
... stationary time series, we examine the Correlogram to decide on the appropriate orders of the AR and MA ...MA process is zero after a point that of an AR process declined ...ARMA ...
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Forecasting Liquidity Ratio of Commercial Banks in Nigeria
... linear time series model, typically an autoregressive integrated moving average [ARIMA(p,d,q)] process, the patterns of autocorrelation and partial autocorrelation could indicate the plausible ...
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Modeling and prediction of time-series of monthly copper prices
... forecasting process difficult [3]. Labys used a structural time-series model to forecast the monthly prices of copper, lead, tin, zinc, and other primary ...forward modeling approaches show ...
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Modeling and Forecasting Africa's GDP with Time Series Models
... Abstract- Forecasting economic growth for developing countries is a problematic task, peculiarly because of particularities they face. The model identification process in this paper yielded a random walk model for ...
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Modeling nonlinearities with mixtures of experts of time series models
... generating process follows a ME construction, maximum likeli- hood parameter estimates are consistent for the true parameters, and asymptotic normal- ity ...data-generating process does not belong to a ME ...
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Financial-Economic Time Series Modeling and Prediction Techniques – Review
... a time series into its linear and nonlinear components, have been shown to be successful for single models, but they show to have many ...linear modeling in order to identify and magnify the existing ...
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Hybrid of ARIMA-GARCH modeling in rainfall time series
... rainfall series of Ipoh and Alorsetar are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, in which large changes often follow large changes, and small ...
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The Ups and Downs of Modeling Financial Time Series with Wiener Process Mixtures
... inhomogeneous time scaling and linear decorrelation between successive price returns, Baldovin and Stella recently proposed a way to build a model describing the time evolution of a financial ...some ...
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On the Application of Bootstrap Method to Stationary Time Series Process
... An excellent introduction to the bootstrap maybe found in the work of [4-9]. Recently, [10,11] have inde- pendently introduced non-parametric versions of the bootstrap that are applicable to weakly dependent sta- tionary ...
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