Deseasonalisation of Data

04/05/2021 1 By indiafreenotes

Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. Any predictable fluctuation or pattern that recurs or repeats over a one-year period is said to be seasonal.

Seasonal effects are different from cyclical effects, as seasonal cycles are observed within one calendar year, while cyclical effects, such as boosted sales due to low unemployment rates, can span time periods shorter or longer than one calendar year.

Seasonality Types

There are three common seasonality types: yearly, monthly and weekly.

(i) Yearly seasonality

Yearly seasonality encompasses predictable changes in demand month over month and are consistent on an annual basis. For example, the purchase of swimsuits and sunscreen prior to the summer months and notebooks and pens leading up to the new school year.

(ii) Monthly seasonality

Monthly seasonality covers variations in demand over the course of a month, like the purchasing of items biweekly when paychecks come in or at the end of the month when there’s extra money in the budget.

(iii) Weekly seasonality

Weekly seasonality is a characteristic of more general product consumption and reflects a host of variables. You may find that consumers buy more (or less) of different products on different days of the week.

Challenges in estimating seasonality indices

The seasonality model illustrated here above is a rather naive approach that work for long smooth seasonal time-series. Yet, there are multiple practical difficulties when estimating seasonality:

  • Time-series are short. The lifespan of most consumer goods do not exceed 3 or 4 years. As a result, for a given product, sales history offers on average very few points in the past to estimate each seasonal index (that is to say the values of S(t) during the course of the year, cf. the previous section).
  • Time-series are noisy. Random market fluctuations impact the sales, and make the seasonality more difficult to isolate.
  • Multiple seasonalities are involved. When looking at sales at the store level, the seasonality of the product itself is typically entangled with the seasonality of the store.
  • Other patterns such as trend or product lifecycle also impact time-series, introducing various sort of bias in the estimation.

In many cases, seasonal patterns are removed from time-series data when they’re released on public databases. Data that has been stripped of its seasonal patterns is referred to as seasonally adjusted or deseasonalized data.