An Epitomized Approach to Possess Promising Predictions by using Time-Series Analysis and Forecasting in R language (original) (raw)
The aim of this work is to exertion a plug-in, formerly named as Time Series Analysis and Forecasting (TSAF) and incorporates this plug-in into R language. The intent behind materializing this plug-in is to establish a firstrated approach to forecast in-advance extrapolations in time series data and to make accurate decisions methodically. The plug-in provides a computationally intelligent environment by accepting a preprocessed time series datasets as input and sense the direction of outputs that will transpire over the coming ages. The internal code structure and implementation details in between the input and output precincts are factorized with the general machine learning, statistical calculation, and visualization packages. The preeminence of this incarnated viewpoint is scientifically verified over timeseries datasets archived in UCI repositories. The results enabled from these datasets pertain to revive qualitative nature of forecasting, which helps the users to predict or foresee changing domain trends and thereby make strategic decisions and hopefully gain lifelong encroachments in this process.
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