Time Series Data Analysis
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The data is considered in three types.
Time series data analysis. Time series datasets record observations of the same variable independent variable an independent variable is an input assumption or driver that is changed in order to assess its impact on a dependent variable the outcome. In investing a time series tracks the movement of the chosen data points over a specified period of time with data points. Time series data means that data is in a series of particular time periods or intervals. A time series is a sequence of numerical data points in successive order.
Most commonly a time series is a sequence taken at successive equally spaced points in time. A time series is simply a series of data points ordered in time. A time series is a series of data points indexed or listed or graphed in time order. Time series data analysis is the analysis of datasets that change over a period of time.
This section will give a brief overview of some of the more widely used techniques in the rich and rapidly growing field of time series modeling and analysis. Time series analysis is a statistical technique that deals with time series data or trend analysis. Time series analysis accounts for the fact that data points taken over time may have an internal structure such as autocorrelation trend or seasonal variation that should be accounted for. In a time series time is often the independent variable and the goal is usually to make a forecast for the future.
Examples of time series are heights of ocean tides counts of sunspots and the daily closing value of the dow jones industrial average. Thus it is a sequence of discrete time data. A set of observations on the values that a variable takes at different times.
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