Case Study in Travel Business - Time Series Analysis with Seasonal Data
summary
Case study on B2B worldwide travel bookings shows a concrete pipeline: decompose series into trend seasonality and noise using the open source Seasonal library estimate periods by a time domain Fourier approach and run decomposition (minutes to ~half an hour on modest data) compute upper and lower control bands set at 1.28 standard deviation flag days that exceed bands crosscheck against Google Calendar and generate a daily report; method works but some residual seasonality remains.
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chapters (9)
- 0:00Introduction
- 1:47Time series analysis
- 3:14Seasonal data in travel
- 8:12Global travel data challenges
- 10:39Seasonal package introduction
- 13:49Handling seasonality
- 15:41Trends and control units
- 16:45Holiday impact on bookings
- 18:17Q&A
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