For years we have been generating data and it is subject to change as time passes. It becomes very important for researchers to pay attention to the trends in the past to make accurate predictions for the upcoming series of events. A time series is where the time is the independent variable.

Considering the example of stock markets, due to its volatility, it becomes very important for analysts and investors to have access to the most accurate figures to park their money. As simple as it may sound, it is equally challenging. No one knows what news might shoot the price to the top and what could smash it down.

This article will help you understand some basics that need to be understood before stepping into predictive modeling for forecasting.

Below given properties influence the modeling process significantly.

• Autocorrelation
• Seasonality
• Stationarity

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