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The automatic ARIMA modeling-based data aggregation scheme utilizes ARIMA model to predict the data of next several periods at both ordinary sensors and aggregators based on the same amount of recently sensed values.
And we will briefly review some representative data aggregation schemes in Section 2. In this article, we proposed an automatic auto regressive-integrated moving average (ARIMA modeling-based dARIMA modeling-basedme which utilizes time series modatato predict the daggregation schemel periods at both ordinary sensor nodes and aggregators based on the same amount of recent data values.
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As an objective, mutual information measures the expected ability of a model to predict the data.
Applies the constructed model to predict the oligomeric states of the input sequence data.
For each of the 256 (4 X 64) variations of data sets and models, the computer was fed, for example, the first year of data, and then told to use the model to predict the numbers for the second year.
More broadly, the study reveals the potential of combining deliberately generated data with data-driven modelling to predict the behaviour of complex dynamical systems.
This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems.
The ability of each of these dynamic models to predict the observed experimental data was scored.
Moreover, the capacity of the model to predict ternary data is meticulously discussed.
We assessed the ability of this model to predict additional sets of gene expression data.
First, the team looked at breast cancer data and built a model to predict metastasis of breast cancer.
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