References
- Arar, O. F., & Ayan¨, K. (2015). Software defect prediction using cost-sensitive neural network. Applied Soft Computing, 33, 263–277. https://doi.org/https://doi.org/10.1016/j.asoc.2015.04.045
- Beneki, C., & Yarmohammadi, M. (2014). Forecasting exchange rates: An optimal approach. Journal of Systems Science and Complexity, 27(1), 21–28. https://doi.org/https://doi.org/10.1007/s11424-014-3304-5
- Broomhead, D. S., & King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20(2–3), 217–236. https://doi.org/https://doi.org/10.1016/0167-2789(86)90031-X
- Channouf, N., L’Ecuyer, P., Ingolfsson, A., & Avramidis, A. N. (2007). The application of forecasting techniques to modelling emergency medical system calls in Calgary, Alberta. Health Care Management Science, 10(1), 25–45. https://doi.org/https://doi.org/10.1007/s10729-006-9006-3
- Constantino, H., Fernandes, P., & Teixeira, J. P. (2016). Tourism demand modelling and forecasting with artificial neural network models: The Mozambique case study. T´ekhne, 14(2), 113–124. https://doi.org/https://doi.org/10.1016/j.tekhne.2016.04.006
- Gillard, J., & Knight, V. (2014). Using singular spectrum analysis to obtain staffing level requirements in emergency units. Journal of the Operational Research Society, 65(5), 735–746. https://doi.org/https://doi.org/10.1057/jors.2013.41
- Golyandina, N., & Zhigljavsky, A. (2013). Singular spectrum analysis for time series. Springer Science & Business Media.
- Hassani, H., Heravi, S., & Zhigljavsky, A. (2009). Forecasting European industrial production with singular spectrum analysis. International Journal of Forecasting, 25(1), 103–118. https://doi.org/https://doi.org/10.1016/j.ijforecast.2008.09.007
- Hassani, H., Heravi, S., Zhigljavsky, A., & Alexandrovich, A. (2013). Forecasting UK industrial production with multivariate singular spectrum analysis. Journal of Forecasting, 32(5), 395–408. https://doi.org/https://doi.org/10.1002/for.2244
- Hassani, H., Webster, A., Silva, E. S., & Heravi, S. (2015). Forecasting US tourist arrivals using optimal singular spectrum analysis. Tourism Management, 46, 322–335. https://doi.org/https://doi.org/10.1016/j.tourman.2014.07.004
- Hyndman, R. J., & Ullah, M. S. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 51(10), 4942–4956. https://doi.org/https://doi.org/10.1016/j.csda.2006.07.028
- Ibrahim, R., Ye, H., L’Ecuyer, P., & Shen, H. (2016). Modelling and forecasting call center arrivals: A literature survey and a case study. International Journal of Forecasting, 32(3), 865–874. https://doi.org/https://doi.org/10.1016/j.ijforecast.2015.11.012
- Jalal, M. E., Hosseini, M., & Karlsson, S. (2016). Forecasting incoming call volumes in call centers with recurrent neural networks. Journal of Business Research, 69(11), 4811–4814. https://doi.org/https://doi.org/10.1016/j.jbusres.2016.04.035
- Kamenetzky, R. D., Shuman, L. J., & Wolfe, H. (1982). Estimating need and demand for prehospital care. Operations Research, 30(6), 1148–1167. https://doi.org/https://doi.org/10.1287/opre.30.6.1148
- Kumar, U., & Jain, V. (2010). Arima forecasting of ambient air pollutants (o3, no, no2 and co). Stochastic Environmental Research and Risk Assessment, 24(5), 751–760. https://doi.org/https://doi.org/10.1007/s00477-009-0361-8
- Leknes, H., Aartun, E. S., Andersson, H., Christiansen, M., & Granberg, T. A. (2016). Strategic ambulance location for heterogeneous regions. European Journal of Operational Research, 260(1), 122–133. https://doi.org/https://doi.org/10.1016/j.ejor.2016.12.020
- Lowthian, J. A., Jolley, D. J., Curtis, A. J., Currell, A., Cameron, P. A., Stoelwinder, J. U., & McNeil, J. J. (2011). The challenges of population ageing: Accelerating demand for emergency ambulance services by older patients, 1995-2015. Medical Journal of Australia, 194(11), 574. https://doi.org/https://doi.org/10.5694/j.1326-5377.2011.tb03107.x
- Mahmoudvand, R., Alehosseini, F., & Zokaei, M. (2013). Feasibility of singular spectrum analysis in the field of forecasting mortality rate. Journal of Data Science, 11, 851–866.
- Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting Methods and Applications. John Wiley & Sons.
- Matteson, D. S., McLean, M. W., Woodard, D. B., & Henderson, S. G. (2011). Forecasting Emergency Medical Service Call Arrival Rates. The Annal of Applied Statistics, 5(2B)(2B), 1379–1406. https://doi.org/https://doi.org/10.1214/10-AOAS442
- Nickel, S., Reuter-Oppermann, M., & Saldanha-da Gama, F. (2016). Ambulance location under stochastic demand: A sampling approach. Operations Research for Health Care, 8, 24–32. https://doi.org/https://doi.org/10.1016/j.orhc.2015.06.006
- O’Keeffe, C., Nicholl, J., Turner, J., & Goodacre, S. (2011). Role of ambulance response times in the survival of patients with out-of-hospital cardiac arrest. Emergency Medicine Journal, 28(8), 703–706. https://doi.org/https://doi.org/10.1136/emj.2009.086363
- Pakravan, M. R., Kelashemi, M. K., Alipour, H. R. (2011). Forecasting Iran’s rice imports trend during 2009-2013. International Journal of Agricultural Management and Development, 1(1), 39–44.
- Ramos, P., Santos, N., & Rebelo, R. (2015). Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and Computer-integrated Manufacturing, 34, 151–163. https://doi.org/https://doi.org/10.1016/j.rcim.2014.12.015
- Reeves, C. E. Integrated scheduling for ambulances and ambulance crews, PhD thesis, Queensland University of Technology, 2015.
- Rukhin, A. L. (2002). Analysis of time series structure SSA and related techniques. Taylor and Francis.
- Salimi, F., Henderson, S. B., Morgan, G. G., Jalaludin, B., & Johnston, F. H. (2016). Ambient particulate matter, landscape fire smoke, and emergency ambulance dispatches in Sydney, Australia. Environment International, 99, 208–212. https://doi.org/https://doi.org/10.1016/j.envint.2016.11.018
- Sen, P., Roy, M., & Pal, P. (2016). Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an indian pig iron manufacturing organization. Energy, 116(1), 1031–1038. https://doi.org/https://doi.org/10.1016/j.energy.2016.10.068
- Sheela, K. G., & Deepa, S. (2013). Neural network-based hybrid computing model for wind speed prediction. Neurocomputing, 122, 425–429. https://doi.org/https://doi.org/10.1016/j.neucom.2013.06.008
- Shen, H., & Huang, J. Z. (2008). Inter-day forecasting and intraday updating of call center arrivals. Manufacturing & Service Operations Management, 10(3), 391–410. https://doi.org/https://doi.org/10.1287/msom.1070.0179
- Silva, E. S., & Hassani, H. (2015). On the use of singular spectrum analysis for forecasting us trade before, during and after the 2008 recession. International Economics, 141, 34–49. https://doi.org/https://doi.org/10.1016/j.inteco.2014.11.003
- Tratar, L. F., & Strmcnikˇ, E. (2016). The comparison of holt–winters method and multiple regression method: A case study. Energy, 109, 266–276. https://doi.org/https://doi.org/10.1016/j.energy.2016.04.115
- Vile, J. L., Gillard, J., Harper, P. R., & Knight, V. A. (2012). Predicting ambulance demand using singular spectrum analysis. Journal of the Operational Research Society, 63(11), 1556–1565. https://doi.org/https://doi.org/10.1057/jors.2011.160
- WAST. (2016). Welsh ambulance services NHS trust annual report 2015/16.
- Wong, H.-T., & Lai, P.-C. (2014). Weather factors in the short-term forecasting of daily ambulance calls. International Journal of Biometeorology, 58(5), 669–678. https://doi.org/https://doi.org/10.1007/s00484-013-0647-x
- Xiao, Y., Liu, J. J., Hu, Y., Wang, Y., Lai, K. K., & Wang, S. (2014). A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. Journal of Air Transport Management, 39, 1–11. https://doi.org/https://doi.org/10.1016/j.jairtraman.2014.03.004
- Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/https://doi.org/10.1016/S0925-2312(01)00702-0
- Zhou, Z., Matteson, M. S. (2017). Predicting Melbourne ambulance demand using kernel warping. The Annals of Applied Statistics, 10(4), 1977–1996. https://doi.org/https://doi.org/10.1214/16-AOAS961
- Zuidhof, G. M. (2010). Capacity planning of ambulance services: Statistical analysis, forecasting and staffing MSc Disseration, University of Amsterdam.