This is the exercise of identifying the features of Time Series to facilitate the research of Design and Implementation a framework for Time Series Modelling using Multi-Agent Technologies. In the early stage of the research, we have identified many features for Time Series as shown below.
In this version, we have included identification of Holidays and Public data sets such as Rainfall, Temperature etc. Data Normalization techniques and Outlier detection is also identified.
Out of the existing techniques, we have extended the evaluation parameters and advanced techniques such as wavenets, and Graph Neural Networks. Further, to facilitate the operations, we have included the exit condition for the modelling and this enables us to detect the non-ending modelling. In addition, we have added the blocked techniques pre-configuration where users can explicitly define the techniques that should be modelled.
In this research, we are looking into different tools to identify the features of the Time series. Until now we have analysed SQL Server and Azure services. Next month, we will be analysing Weka and Orange tools.
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