#Fuzzytime junktion series#

' Automatic time series forecasting: the forecast package for R,' Therefore, it became an important indicative in the representation of characteristics of seasonality by the models. The confirmation of accuracy by the hybrid models reveals that they provide time series that are able to follow the observed time series profiles with similarities of maximum and minimum values between both series. It is possible to find in this analysis percentage error of 5.0% and efficiency coefficient (Nash-Sutcliffe) of approximately 0.96. By means of statistical analysis of error it is verified that the proposed hybrid models offer perfect adjustments to the observed data at the regions of study, and thus, better comparisons with traditional ones from the literature.

The second hybrid model (HW and ANN) is considered as univariate, i.e. The first hybrid model (ARIMAX and ANN) is made from the physical relations between pressure, temperature and precipitation with the wind speed, that is, this model is considered as multivariate. This work shows two innovative hybrid methodologies capable of performing short and long term wind speed predictions from the mathematical junction of two classical time series models the Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) and the Holt-Winters (HW), both combined with Artificial Neural Networks (ANN).