Also, year to year values will be biased by any changes in seasonal patterns that occur over time. Certain holidays such as Easter and Chinese New Year fall in different periods in each year, hence they will distort observations. WHY CAN'T WE JUST COMPARE ORIGINAL DATA FROM THE SAME PERIOD IN EACH YEAR?Ī comparison of original data from the same period in each year does not completely remove all seasonal effects. Observed data needs to be seasonally adjusted as seasonal effects can conceal both the true underlying movement in the series, as well as certain non-seasonal characteristics which may be of interest to analysts.
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Seasonal adjustment is the process of estimating and then removing from a time series influences that are systematic and calendar related. WHAT IS SEASONAL ADJUSTMENT AND WHY DO WE NEED IT? Other seasonal effects include trading day effects (the number of working or trading days in a given month differs from year to year which will impact upon the level of activity in that month) and moving holiday (the timing of holidays such as Easter varies, so the effects of the holiday will be experienced in different periods each year). Some examples include the sharp escalation in most Retail series which occurs around December in response to the Christmas period, or an increase in water consumption in summer due to warmer weather. Both types of series can still be seasonally adjusted using the same seasonal adjustment process.Ī seasonal effect is a systematic and calendar related effect. The main difference between a stock and a flow series is that flow series can contain effects related to the calendar (trading day effects). Manufacturing is also a flow measure because a certain amount is produced each day, and then these amounts are summed to give a total value for production for a given reporting period. For example, surveys of Retail Trade activity. For example, the Monthly Labour Force Survey is a stock measure because it takes stock of whether a person was employed in the reference week.įlow series are series which are a measure of activity over a given period. Time series can be classified into two different types: stock and flow.Ī stock series is a measure of certain attributes at a point in time and can be thought of as “stocktakes”. Data collected irregularly or only once are not time series.Īn observed time series can be decomposed into three components: the trend (long term direction), the seasonal (systematic, calendar related movements) and the irregular (unsystematic, short term fluctuations). This is because sales revenue is well defined, and consistently measured at equally spaced intervals. For example, measuring the value of retail sales each month of the year would comprise a time series. A time series is a collection of observations of well-defined data items obtained through repeated measurements over time.
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