Forecasting Daily Number of User Problem Reports of Junyi Academy for Efficient Staff Allocation

Transcription

1 Forecasting Daily Number of User Problem Reports of Junyi Academy for Efficient Staff Allocation Group Chiali Sherry Elisa Emily

2 Background Junyi Academy is a platform offering online learning resource for all ages! It provides practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. With high utilization ratio of the practice exercises, Junyi receives problems reported by users, which is called user problem reports. And all the reports will be checked and then be distributed to the responsible team by operation team. Since there is no full-time staff dealing with user problem reports. We want to help them better allocate manpower, so that every report could be resolved on time.

3 Business & Forecasting Goal There is no full-time staff dealing with user problem reports. Thus, if daily reports are over 23 (which is the average, it is very likely that reports cannot be solved on that day. number of reports solved) Business Goal With the forecast of the number of user problem reports, the manager could better allocate staff and their working loading. Client: The manager of Junyi Academy Operation Team Stakeholder: Junyi s employees, Students, Teachers, Parents Forecasting Goal We are forecasting (F t ): the daily number of user problem report in the coming week Y t : daily number of problem report Forecast horizon: h=1,2,3,4,5,6,7 (a-week-ahead forecasts) Goal type: forward-looking

4 Business Data & Forecasting Goal Source: Junyi Academy Time Period: ~ Amounts of Rows: 77 Series: Received: daily number of problem report (with weekly seasonality and no trend) New registration user: daily number of new registered user Active user: daily number of active user, who finished one exercise on that day

5 Business Data Preprocessing & Forecasting Goal Create seasonal dummies Create binomial series for school-day Create lag series: lag7_registration and lag7_activeuser Find the outlier and create a binomial series for outlier - Based on boxplot of series Received - Typhoon day-off

6 Methods Seasonal Naive SES, MA, Holt-Winter s (no trend), MLR, NN Benchmark Try forecasting methods which can deal with series with seasonality but no trend Select methods (based on the performance on RMSE and MAPE) MLR & NN have better and similar performance

7 Methods & Evaluation T r a i n i n g V a l i d a t i o n RMSE MAPE RMSE MAPE Seasonal Naive [ Benchmark ] SES Trailing MA Holt-Winter (ANA) Regression Model with external data Neural Network (5-25-1)

8 Methods Seasonal Naive SES, MA, Holt-Winter s (no trend), MLR, NN Benchmark Try forecasting methods which can deal with series with seasonality but no trend MLR Try different models different combination of external series Not include lag-7 Registration predictor: Lag-7_Active user, SchoolDay, Outlier, Seasonal dummies Select methods (based on the performance on RMSE and MAPE) MLR & NN have better and similar performance NN Try different models with different combination of external series and deseasonalizing Include all external data and seasonal dummies Based on the time plot and ability of capturing peaks, we chose this model.

9 Methods & Evaluation MLR with external series NN 1 hidden layers & 25 neurons using XLMiner v.s. R

10 Results NN 1 hidden layers & 25 neurons using R 23 NN - Training RMSE MAPE

11 Limitations & Recommendations Limitations We cannot forecast the exact number of peak values, but we can capture where the peaks are. There will be lower cost for over-forecast than under-forecast. However, our model tends to be under-forecast at the peaks. Over-forecast: Extra part-time workers hired to deal with reports will be assigned with other tasks. Under-forecast: Staff will not be able to finish their work on time and left problem reports unsolved. Recommendations According to the limitations, we suggest Junyi could run our predictive model on every Friday to forecast the next week s report number. Thus, it will help them decide whether to allocate extra staff to deal with reports arrange people to check the content of the questions before they are released.