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Automated Time Series Forecasting Process

Automated Time Series Forecasting Process

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Automated Time Series Forecasting Process

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  1. Automated Time Series Forecasting Process ValentinTodorov January 23, 2013 Assurant Specialty Property

  2. Objective • Develop a time series forecast of incoming calls to the company’s call centers • Create a forecast for calls related to mortgages and a separate one for HELOCs • Re-forecast weekly using the most recent actual call data • Distribute results to managers in the call centers • Implement the forecasting process within one month • Budget $0 …. excluding the man-hours to develop the process All of that should be accomplished with SAS

  3. Challenging and messy Time consuming Error prone

  4. A sleek solution Dropbox Every Monday morning an employee from our call center sends via FTP a file with the previous week’s actual call volumes Use a CRON job to monitor the dropbox, and test every 5 minutes for the existence of a specific file. If a file is present, execute a SAS code Import and clean the data, re-forecast and send results via email The only human in this process is the one who sends the file via FTP CRON is a job scheduler in UNIX. For more information see http://en.wikipedia.org/wiki/Cron

  5. Dropbox • Folder located on the servers • CRON job performs a test every 5 minutes for the existence of files [[ -f “/sas/shared/dropbox/u99/MortgCo/MortgCo_${source_type}_update.csv” ]] && MortgCo_haz_update.csv – Calls related to mortgage loans MortgCo_heloc_update.csv – Calls related to HELOC loans • If files exist, BASH code is executed which kicks off the SAS code

  6. SAS code • Import new files with actual call volume data from previous week • Check for data integrity and consistency • Append to historical files and clean up • Run the forecasts • Package the data • Send forecasts via email to managers in call center

  7. Call volume has a consistent daily pattern • The call volume follows a pattern that repeats on a weekly basis • The pattern is interrupted by public holidays, which are visible on the graph • No calls during weekends

  8. The forecasting process is driven by macros

  9. Check data integrity of incoming files • Calculate length of latest file with actual data – should be 7 days • Check if the dates in the file are the last 7 days If any condition fails, stop the process and send warning emails!

  10. ARIMA Forecast • The model is ARIMA • Mortgages: AR (5,10,15,20) MA (5) with a difference = 5 • HELOC: AR (5,10,15) MA (4) with a difference = 5 • Developed using two years of historical data • The forecast has 24% higher accuracy compared to the incumbent model • Accuracy: Number of days forecast has a variance of less than 8%

  11. Email final results

  12. ValentinTodorov vatodorov@gmail.com http://www.linkedin.com/in/vatodorov/