A time series is a sequence of observations that are ordered in time.
There are two kinds of time series data:

• Continuous (an observation every instant of time)
• Discrete (an observation- usually regular - spaced intervals)

There are several examples of time series data that are well known:

• Economics - weekly share prices, monthly profits
• Meteorology - daily rainfall, wind speed, temperature
• Sociology - crime figures , employment figures

The main features of time series are explained below:

Trend Component
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. A simple way of detecting trend in seasonal data is to take averages over a certain period. If these averages change with time, we can say that there is an evidence of a trend in the series.

Cyclical Component
The cyclical component describes any regular fluctuations. A non-seasonal component varies in a recognizable cycle.

Seasonal Component
The seasonal component, often referred to as seasonality, is the component of variation in a time series which is dependent on the time of year. It describes any regular fluctuations with a period of less than one year. For example, the costs of various types of fruits and vegetables, unemployment figures and average daily rainfall, all show marked seasonal variation.

For example, sales of a company can rapidly grow over years but they still follow consistent seasonal patterns (e.g., as much as 25% of yearly sales each year are made in December, whereas only 4% in August).

Irregular Component
The irregular component is one that remains after the other components of the series (trend, seasonal and cyclical) have been accounted.

The graph below shows for each calendar month the behavior of seasonal and irregular influences, that is, the original data with within month influences and trend removed. Values of the seasonal influences above the neutral line indicate seasonally high months; those below are seasonally low.

It can be seen from the above Graph that January is the seasonally lowest month for Retail Trade. With the exception of May, months February through October are all seasonally low. May had been approximately seasonally neutral but has become seasonally low in more recent years. November and, to a much greater extent, December are seasonally high months due to the increased retail trade associated with the pre-Christmas period of the year, and there is a compensating downwards movement in January.

Smoothing Techniques
Smoothing techniques are used to reduce irregularities (random fluctuations) in time series data. Smoothing can remove seasonality and makes long term fluctuations in the series stand out more clearly.
There most common approaches to smoothing are:

Exponential Smoothing Exponential smoothing is a smoothing technique used to reduce irregularities (random fluctuations) in time series data, thus providing a clearer view of the true underlying behavior of the series. It also provides an effective means of predicting future values of the time series (forecasting).

Moving Average Smoothing
Moving average smoothing is a smoothing technique used to make the long-term trends of a time series clearer. When a variable, like the number of unemployed, or the cost of strawberries, is graphed against time, there are likely to be considerable seasonal or cyclical components in the variation. These may make it difficult to see the underlying trend. These components can be eliminated by taking a suitable moving average.By reducing random fluctuations, moving average smoothing makes long term trends clearer.