Time Serie - Seasonality (Cycle detection)

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1 - About

Seasonality is a cycle in time serie.

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3 - Methods

3.1 - Regression with Season as Dummy Variable

The season is used a discreet regression variable and code it as dummy variables).

For instance:

  • 1 if it's the season
  • 0 if it's not the season

If you have more than one type of season, then you may need various dummy variables.

3.2 - Seasonal Index

If you have five Decembers, then you should have five June. Having five of some month and six of other months will skew the seasonal indices.

Calculate a seasonal index for each month on past data.

If the average monthly sales is 100%, then the value in each month shows how that month compares to the average. Example:

  • January can be 54.8% of the average month whereas December can 256.19% of the average month.

Do a normal regression and apply then this index to the forecast value.

3.3 - Holt-Winters method

The Holt-Winters method was designed to handle data where there is a conventional seasonal cycle across the course of a year, such as monthly seasonality. However, many series have multiple cycles: the demand for electricity will have hourly (patterns across the hours of a day), daily (patterns across the days of the week), and monthly cycles. Similar patterns occur in the number of calls received by call centers or the workload faced by hospitals.

3.4 - Fast Fourier Transform

FFT is a good tool to detect seasonality if we have a good amount of historical data. See http://nerds.airbnb.com/anomaly-detection

4 - Statistics

4.1 - R Squared

A R-Squared value of 0.0999 will means that straight-line forecasting is not going to yield an accurate forecast.

5 - Documentation / Reference

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data/type/time/serie/seasonality.txt · Last modified: 2018/09/22 11:16 by gerardnico