Both types of series can still be seasonally adjusted using the same seasonal adjustment process. A seasonal effect is a systematic and calendar related effect. 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.
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.
Seasonal adjustment is the process of estimating and then removing from a time series influences that are systematic and calendar related. 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.
A comparison of original data from the same period in each year does not completely remove all seasonal effects. Certain holidays such as Easter and Chinese New Year fall in different periods in each year, hence they will distort observations. Also, year to year values will be biased by any changes in seasonal patterns that occur over time. For example, consider a comparison between two consecutive March months i.
This comparison ignores the moving holiday effect of Easter. Easter occurs in April for most years but if Easter falls in March, the level of activity can vary greatly for that month for some series.
This distorts the original estimates. A comparison of these two months will not reflect the underlying pattern of the data. The comparison also ignores trading day effects. Many people confuse cyclic behaviour with seasonal behaviour, but they are really quite different. If the fluctuations are not of fixed period then they are cyclic; if the period is unchanging and associated with some aspect of the calendar, then the pattern is seasonal.
In general, the average length of cycles is longer than the length of a seasonal pattern, and the magnitude of cycles tends to be more variable than the magnitude of seasonal patterns. The top plot shows the famous Canadian lynx data — the number of lynx trapped each year in the McKenzie river district of northwest Canada These show clear aperiodic population cycles of approximately 10 years. The cycles are not of fixed length — some last 8 or 9 years and others last longer than 10 years.
The middle plot shows the monthly sales of new one-family houses sold in the USA The bottom plot shows half-hourly electricity demand in England and Wales from Monday 5 June to Sunday 27 August Here there are two types of seasonality — a daily pattern and a weekly pattern. If we collected data over a few years, we would also see there is an annual pattern. Trend A trend exists when there is a long-term increase or decrease in the data.
It does not have to be linear. There is a trend in the antidiabetic drug sales data shown in Figure 2. Seasonal A seasonal pattern occurs when a time series is affected by seasonal factors such as the time of the year or the day of the week.
Seasonality is always of a fixed and known frequency.
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