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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 quantitative forecasting approach is further sub-divided into two parts: causal techniques and time series techniques.
In the literature, the oil price – food price relations are normally examined by means of the standard time series techniques of cointegration, error-correction modelling and Granger causality.
To capture the short term dynamics of the return distribution, and the long run equilibrium relationship between the stock markets, the present study uses various time series techniques.
Furthermore, temporal similarity measures cannot directly apply the efficient time series techniques, requiring new approaches to deal with these heterogeneous sequences.
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The proposed Fourier series technique is demonstrated capable of generating feasible and near-optimal trajectories.
We employ the power series technique to solve the governing differential equations with variable coefficients.
The Fourier series technique is flexible in generating various shapes rather than using one global shape.
The analytical solutions are then obtained by using the power series technique.
The methodology of the solution is based on the Chebyshev series technique.
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