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http://dx.doi.org/10.3745/KTCCS.2019.8.4.79

A Time-Series Data Prediction Using TensorFlow Neural Network Libraries  

Muh, Kumbayoni Lalu (금오공과대학교 IT융복합공학과)
Jang, Sung-Bong (금오공과대학교 산학협력단)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.8, no.4, 2019 , pp. 79-86 More about this Journal
Abstract
This paper describes a time-series data prediction based on artificial neural networks (ANN). In this study, a batch based ANN model and a stochastic ANN model have been implemented using TensorFlow libraries. Each model are evaluated by comparing training and testing errors that are measured through experiment. To train and test each model, tax dataset was used that are collected from the government website of indiana state budget agency in USA from 2001 to 2018. The dataset includes tax incomes of individual, product sales, company, and total tax incomes. The experimental results show that batch model reveals better performance than stochastic model. Using the batch scheme, we have conducted a prediction experiment. In the experiment, total taxes are predicted during next seven months, and compared with actual collected total taxes. The results shows that predicted data are almost same with the actual data.
Keywords
Artificial Neural Networks; Time-Series Data; Data Prediction; TensorFlow;
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