Understanding Pattern Analysis and its Components
Pattern Analysis with Time Series Data involves nature identification represented by a sequence of observations and forecasting including prediction of future values Time- Series variable. Time Series Pattern Analysis consists of systematic pattern data called as a set of identifiable components and random noise error which makes pattern identification difficult.
Components of Pattern Analysis
- Trend Analysis
- Seasonality Analysis
Trend Analysis Overview
The trend is described as a linear function to eliminate non-linearity through a log or exponential functions. If an error occurs in trend, then smoothing is required such as a moving average with components replacement of the series with a simple or weighted average.
Seasonality Analysis Overview
It consists of autocorrelation correlograms to display serial correlation for consecutive lags and examining correlograms, removal of serial dependencies and partial autocorrelations.