TIME SERIES ANALYSIS.
A time series is a sequence of observations which are ordered in time (or space). If observations are made on some phenomenon throughout time, it is most sensible to display the data in the order in which they arose, particularly since successive observations will probably be dependent.
The main purposes of time series is to identifying the nature of the phenomenon represented by the sequence of observations, and forecasting (predicting future values of the phenomenon/variable from past observations)
Time series are best displayed in a scatter plot. The series value X is plotted on the vertical axis and time t on the horizontal axis. Time is called the independent variable (in this case however, something over which you have little control).
There are two kinds of time series data:
1. Continuous, where we have an observation at every instant of time, e.g. lie detectors, electrocardiograms. We denote this using observation X at time t, X(t).
2. Discrete, where we have an observation at (usually regularly) spaced intervals. We denote this as Xt.
3.
Examples
Economics - weekly share prices, monthly profits
Meteorology - daily rainfall, wind speed, temperature
Sociology - crime figures (number of arrests, etc), employment figures
Time series contain at least one of the following four components:
1. Trend Component /Secular trend
Trend is a long term movement in a time series. It is the underlying direction (an upward or downward tendency) and rate of change in a time series, when allowance has been made for the other components.
A simple way of detecting trend in seasonal data is to take averages over a ...