Automated Time Series Forecasting Process Valentin Todorov January 23, 2013 Assurant Specialty Property

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 2

Challenging and messy Time consumingError prone 3

A sleek solution Dropbox 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 Every Monday morning an employee from our call center sends via FTP a file with the previous week’s actual call volumes Import and clean the data, re- forecast and send results via CRON is a job scheduler in UNIX. For more information see The only human in this process is the one who sends the file via FTP 4

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 5

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 to managers in call center 6

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 7

The forecasting process is driven by macros 8

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 9 If any condition fails, stop the process and send warning s!

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% 10

final results 11

Valentin Todorov 12