A Comparative study of Hybrid Neural Network and ARIMA Models with application to forecasting intra-day child-line calls in Kenya
| dc.contributor.author | Wang’ombe, Grace Wairimu | |
| dc.date.accessioned | 2023-05-22T09:33:48Z | |
| dc.date.available | 2023-05-22T09:33:48Z | |
| dc.date.issued | 2022 | |
| dc.description | Submitted in total fulfillment of the requirements for the degree of Masters of Science in Statistical Science of Strathmore University | |
| dc.description.abstract | Background: For successful staffing and recruiting of call centre professionals, precise forecasting of the number of calls arriving at the centre is crucial. These projections are needed for various periods, both short and long-term. Benchmark time series models such as ARIMA and Holt-Winters used in forecasting call centre data are outperformed in long term forecasts, especially when the data is not stationary. Advanced models such as the ANNs can pick up on the random peaks or outlying periods better than the benchmark time–series models. The hybrid methodology combines the strengths of the benchmark time–series and advanced models, thus improving overall forecasts. Objective: The study’s primary goal was to assess the superiority of a Hybrid ARIMAANN model over its constituent models in forecasting Childline call centre data in Kenya. Methods: The ARIMA, ANN and hybrid ARIMA-ANN models were used in the call centre data forecasting. The cross-validation technique was used to create forecasting accuracy metrics which are then compared. In ARIMA, the Box-Jenkins methodology is used to fit the model whereas the neural network element of the hybrid model and the ANN were modelled using the feed-forward Neural Network Autoregressive(NNAR) structure. Results: The Seasonal ARIMA - ANN model outperformed the ARIMA model in short term forecasts and the ANN model in long term forecasts. The Diebold-Mariano test indicated a significant difference between the hybrid and ANN forecasts, whereas the difference between the hybrid and ARIMA forecasts was not significant. Conclusion: The Hybrid model was able to adapt both of its constituent models’ advantages to better its performance. These results are helpful as call centres can be able to use one model which is robust enough to create accurate forecasts rather than the benchmark models. | |
| dc.identifier.uri | http://hdl.handle.net/11071/13168 | |
| dc.language.iso | en | |
| dc.publisher | Strathmore University | |
| dc.title | A Comparative study of Hybrid Neural Network and ARIMA Models with application to forecasting intra-day child-line calls in Kenya | |
| dc.type | Thesis |
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