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Source code sistem informasi perusahaan5/26/2023 ![]() ![]() Lets you select filters based on attributes, such as location, brand, category, size, color, sentiment, quality, etc.Enables customized forecast adjustments that aren't limited by the structure of the forecasting hierarchy.Addresses problems that have both time series characteristics and nonlinear relationships between dependent and independent variables using stacked model (neural network + time series) forecasting.Provides a multistage (neural network/regression + time series) framework for creating a forecasting methodology that combines signals from different types of models.Includes a panel series neural network framework for generating features and training a neural network.Neural network/machine learning modeling strategy nodes Is optimized for the machine on which it is running, so users don’t have to rewrite code for different machines.Executes each time series on one thread of a node, and each node executes the compiled script for each of its assigned time series.Shuffles the data so that each time series is copied into the memory of a single computing node.Scripting language enables distributed, in-memory time series analysis.Automatically generates large quantities of statistically based forecasts in a distributed, in-memory environment.Large-scale time series analysis & forecasting in a distributed environment
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