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Fig. 3 Time series average summer temperature pattern (1991 2010).

Exp  real9597 is the time series average of that ratio from 1995 through 1997.

This preprocessing step implies local time-series averaging, which can generate spurious synchronization between neighboring voxels.

Most real-time EV prediction systems are based on traditional time series analysis techniques such as moving average or autoregressive integrated moving average (ARIMA) models.

Note that both time series are averaged with a moving window of 10 days.

Temperature time series was averaged on a weekly basis.

The quantitative forecasting approach is further sub-divided into two parts: causal techniques and time series techniques.

It is reported that the time series analysis based techniques like the autoregressive integrated moving average (ARIMA) is one of the most precise methods for the prediction of traffic flow when compared to other available techniques as mentioned above [27].

Time series techniques have been widely used in environmental contexts.

All predicting time series techniques can be divided into two board categories: Qualitative and Quantitative [11].

The use of multiple nonlinear time series techniques, however, suggests an alternative interpretation, namely deterministic chaos.

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