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The uncertainties are predicted by time series methods.
Fuzzy time series methods are based on fuzzy set theory.
Table 3 shows the out-of-sample forecast results using linear and nonlinear time series methods.
In addition, time series methods are widely used to predict electricity prices.
Time series methods, including short-term forecasting methods like ARIMA model, GARCH model.
Fuzzy time series methods are effective techniques to forecast time series.
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Time-series methods have been used to account for and correct temporal correlation of the data.
It may then be better to employ time-series methods to analyse and present the data.
In this paper, we use time-series methods to model and generate hourly temperature, wind-speed, and solar-radiation forecasts at 61 locations in the United States.
While there are well-established time-series methods for tracking forest disturbance, these have largely been developed using moderate resolution EO data.
Adaptive control, sine testing and time-series methods are not considered due to physical system limitations and memory and processing limitations of the selected micro-processors.
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