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Since the eigenvalues of covariance matrix C are sorted in descending order, the signal of the time series can be represented by the first several RCs, while the remaining RCs being treated as noise.
In this paper, we propose a method to alleviate the disadvantages of the existing methods so that similarly shaped time series are included in the cluster.
In this paper, a more general model is considered in the analysis of the hydrological or meteorological time series conditional to the historical available information: it is assumed that the data is generated from a normal, a gamma or a beta distribution, with conditional mean and variance, given respectively, by E [[Y.sub.t]|[Y.sub.t-1]] and V [[Y.sub.t]| [Y.sub.t-1]], where the index tis related to time.
This reality requires the classification of time series data into states or conditions.
SS model is based on idea that time series will have the same gradient (slope) in the next time period like in previous.
In our model of online time series prediction, we set a dense layer to map the outputs to the target prediction, which is formulated as
Specifically, we propose computation of the new time series with a nearest-neighbor moving-average kernel that uses information from the "past" and "future" values to accurately capture the intrinsic dynamics of the short time series.
A point to be kept in mind is that the B-J methodology is applicable only to stationary variables.There are three primary stages in building a Box-Jenkins time series model: model identification, model estimation and model validation.
However, the best of our knowledge, there is still a lack of information on exponential smoothing time series modeling for forecasting production amounts of sesame, sunflower and soybean in the World and in Turkey.
Incorporation is a mapping that transfers a set M of one-dimension time series [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] to a multidimensional [[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]] with lagged vectors [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], where i = 1, ...
Thus the addition model of the time series can be generalized as follows: