Data Mining through Time Series Forecasting Algorithm

G.N.K. Suresh Babu, Dr.S.K. Srivatsa


In this paper we describe time series forecasting which is used for determining future predictions. Any variable that is measured over time in sequential order is called a time series. We analyze time series to detect patterns. The patterns help in forecasting future values of the time series. Many quantities in nature fluctuate in time. Examples are the stock market, the weather, seismic waves, sunspots, heartbeats, and plant and animal populations. Until recently it was assumed that such fluctuations are a consequence of random and unpredictable events. With the discovery of chaos, it has come to be understood that some of these cases may be a result of deterministic chaos and hence predictable in the short term and amenable to simple modeling. Many tests have been developed to determine whether a time series is random or chaotic, and if the latter, to quantify the chaos. If chaos is found, it may be possible to improve the short-term predictability and enhance understanding of the governing process. Forecasting is fundamental to decision-making. There are three main methods:? Subjective forecasting is based on experience, intuition, guesswork and a good supply of envelope-backs.? Extrapolation is forecasting with a rule where past trends are simply projected into the future.? Causal modeling (cause and effect) uses established relationships to predict, for example, sales on the basis of advertising or prices.

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