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Data Mining through Time Series Forecasting Algorithm

Author(s): Thudum Venkatesh | Kondabathula DurgaCharan | Krishna Kumar Singh

Journal: International Journal of Computer & Electronics Research
ISSN 2320-9348

Volume: 2;
Issue: 4;
Start page: 487;
Date: 2013;
Original page

Keywords: Data Mining | Analysis | Time series | Prediction | Assessment | Modeling | Decision making | Forecast.

In this paper, we describe future expectations and estimates used to determine the time series. A variable is a time period measured in the time series. We analyze the samples to determine the time series. Models to help predict future values ​​of the time series. Nature of the change in the quantity of time. Examples of the stock market, weather, seismic waves, sunspots, heartbeats, and plant and animal populations. Until recently, such fluctuations are thought to be the result of random and unpredictable events. The discovery of chaos, confusion, and thus the decision of the cases before it in the short term as a result of the general model can be tailored to understand. A variety of tests have been developed to ensure that a time series of random or chaotic, and then, if the quantum chaos. If confusion, short-term assessment of the development and the opportunity to raise awareness of public policy.   
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