PRODUCTION DATA BASED FINANCIAL LEVEL ANALYSIS THROUGH RECURRENT NEURAL BASED MULTIVARIATE DATA SELECTION ALGORITHM USING DATA MINING APPLICATION

Abstract

The Financial data series, to mimic the excessive fluctuation, is near a hard type of statistics on some imitation of prediction. To all about the rumors, through the additional elements of the next within the range of available communication network, various forms of data, the fluctuations, production companies are effectively related inventory, equipment, and the need to use the personnel to increase the kind of financial information delay, to enhance its product. Businesses use the ratio of currency that is similarly based on the evaluation of the business. While maintaining the data level business's effectiveness, these ratios have been implemented based on a study to determine to integrate into an industrial process properly. Previous algorithm for Long Short Term Memory (LSTM), then Gated Recurrent Unit (GRU); in particular, will concentrate on certain types of twin networks. The former is the case of many, series is predicted, the second is the difference between excellent large newborns and offer original. To analyze the application of special neural networks, especially Recurrent Neural Networks (RNNs). In the forecasts gathered in the causal era, the structure of high-risk economic variables creates the motivation behind using multivariate relevant data. Previous algorithm supported GRU after, empirical results are particularly suitable for use because mimicking the performance of coach era. The LSTM reproduces them with the same accuracy. Since the clinical data set of the real world has not yet been shown to the synthetic data set, but the reliability is high, also, due to the use of the absence of a value of between series analysis, the data collection of artistic expression even in a state, the classification task of periodic sequence to provide useful insights experiments.