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There are several methods to model and predict time series; the traditional statistical methods which introduce linear predictions for future values of the variables, such as: Moving Average, Weighted Average, ARIMA, Regression Method, Markov Chain, Neural Networks, and etc.
In addition, in recent years, artificial neural networks have been employed to predict time series.
MR imaging may allow clinicians to predict time required before athletes can return to competition.
A method to predict time to fatigue failure under the test conditions is proposed.
WEKA's forecasting tool employs a set of regression based learning methods to model and predict time series data.
It is shown that the RSES method enables one to more accurately predict time series with sudden level shifts.
Similar(8)
Combining models which predict time-varying risks associated with hospitalization with traditional disease-specific prediction rules should theoretically result in much more accurate predictive tools which can be used to update risk estimates as an admission progresses over time.
This paper presents a model to predict time-dependent instability of arbitrarily oriented wellbores.
A simple, explicit and exact expression was found to predict time-to-complete dissolution (TCD).
The proposed automatic attention estimation system is able to predict time-varying attention A u (t) of a person u by observing his body and facial features.
A poromechanical composite model is developed to predict time-dependent stress and strain fields in freezing concrete containing aggregates with undesirable combinations of geometry and constitutive properties.
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