Skip to main content
SpringerLink
Log in

Long-Term and Multi-Step Ahead Call Traffic Forecasting with Temporal Features Mining

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

An accurate call traffic forecasting can help the call center to schedule and manage its employees more scientifically. Meanwhile, to meet the needs that some tasks in the call center require the prediction of call traffic in different time buckets for a future long term, it is necessary to forecast the call traffic in a long-term and multi-step way. However, existing forecasting methods cannot solve this problem as (1) Most existing methods merely focus on short-term forecasting for the next hour or the next day. (2) The temporal features of call traffic are ignored, which leads to a lower accuracy in long-term forecasting. Hence, in this paper, we propose a holistic solution for forecasting long-term multi-step ahead call traffic. In our method, we give a categorized way for temporal features by studying the call traffic data. After data preprocessing, we develop an extraction method for temporal features extraction for training the forecasting model. We propose two forecasting strategies based on taking different types of features as input. Experimental results on the real-world call traffic dataset show the effectiveness of our solution, including data preprocessing, temporal features mining, and the forecasting model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Aksin Z, Armony M, Mehrotra V (2007) The modern call center: a multi-disciplinary perspective on operations management research. Prod Oper Manag 16(6):665–688

    Article  Google Scholar 

  2. Barrow DK (2016) Forecasting intraday call arrivals using the seasonal moving average method. J Bus Res 69(12):6088–6096

    Article  Google Scholar 

  3. Bianchi L, Jarrett J, Hanumara RC (1998) Improving forecasting for telemarketing centers by arima modeling with intervention. Int J Forecast 14(4):497–504

    Article  Google Scholar 

  4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  5. Cao L, Ma K, Cao B, Fan J (2019) Forecasting long-term call traffic based on seasonal dependencies. In: International conference on collaborative computing: networking, applications and worksharing. Springer, pp 231–246

  6. Cao X, Dong S, Wu Z, Jing Y (2015) A data-driven hybrid optimization model for short-term residential load forecasting. In: 2015 IEEE International conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing. IEEE, pp 283–287

  7. Cezik MT, L’Ecuyer P (2008) Staffing multiskill call centers via linear programming and simulation. Manag Sci 54(2):310–323

    Article  MATH  Google Scholar 

  8. Feigelson ED, Babu GJ, Caceres GA (2018) Autoregressive times series methods for time domain astronomy. Front Phys China 6:80

    Google Scholar 

  9. Gao H, Huang W, Yang X (2019) Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data. Intell Autom Soft Comput 25(3):547–559

    Google Scholar 

  10. Hyndman R, Koehler AB, Ord JK, Snyder RD (2008) Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media

  11. Ibrahim R, Ye H, L’Ecuyer P, Shen H (2016) Modeling and forecasting call center arrivals: a literature survey and a case study. Int J Forecast 32(3):865–874

    Article  Google Scholar 

  12. Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215–236

    Article  Google Scholar 

  13. Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2017) Short-term residential load forecasting based on lstm recurrent neural network. IEEE Transactions on Smart Grid 10(1):841–851

    Article  Google Scholar 

  14. Kuang L, Yan H, Zhu Y, Tu S, Fan X (2019) Predicting duration of traffic accidents based on cost-sensitive Bayesian network and weighted k-nearest neighbor. J Intell Transp Syst 23(2): 161–174

    Article  Google Scholar 

  15. Kuang L, Yan X, Tan X, Li S, Yang X (2019) Predicting taxi demand based on 3d convolutional neural network and multi-task learning. Remote Sens 11(11):1265

    Article  Google Scholar 

  16. Li C, Zheng X, Yang Z, Kuang L (2018) Predicting short-term electricity demand by combining the advantages of arma and xgboost in fog computing environment. Wirel Commun Mob Comput, 2018

  17. Lu CJ, Lee TS, Chiu CC (2009) Financial time series forecasting using independent component analysis and support vector regression. Decis Support Syst 47(2):115–125

    Article  Google Scholar 

  18. Marino DL, Amarasinghe K, Manic M (2016) Building energy load forecasting using deep neural networks. In: IECON 2016-42nd Annual conference of the IEEE industrial electronics society. IEEE, pp 7046–7051

  19. McGill R, Tukey JW, Larsen WA (1978) Variations of box plots. Am Stat 32(1):12–16

    Google Scholar 

  20. Oreshkin BN, Réegnard N, L’Ecuyer P (2016) Rate-based daily arrival process models with application to call centers. Oper Res 64(2):510–527

    Article  MATH  Google Scholar 

  21. Pearson K (1895) Note on regression and inheritance in the case of two parents. Proc R Soc Lond 58:240–242

    Article  Google Scholar 

  22. Rowlett R (2005) How many?: a dictionary of units of measurement. R Rowlett

  23. Ryu S, Noh J, Kim H (2016) Deep neural network based demand side short term load forecasting. Energies 10(1):3

    Article  Google Scholar 

  24. Shumway RH, Stoffer DS (2017) Time series analysis and its applications: with R examples. Springer

  25. Thompson HE, Tiao GC (1971) Analysis of telephone data: a case study of forecasting seasonal time series. Bell J Econ Manag Sci, 515–541

  26. Wu Y, Tan H, Qin L, Ran B, Jiang Z (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C: Emerg Technol 90:166–180

    Article  Google Scholar 

  27. Yin Y, Chen L, Wan J, et al. (2018) Location-aware service recommendation with enhanced probabilistic matrix factorization. IEEE Access 6:62,815–62,825

    Article  Google Scholar 

  28. Yin Y, Chen L, Xu Y, Wan J, Zhang H, Mai Z (2019) Qos prediction for service recommendation with deep feature learning in edge computing environment. Mob Netw Appl, 1–11

  29. Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7):1501

    Article  Google Scholar 

  30. Zhang P, Wu X, Wang X, Bi S (2015) Short-term load forecasting based on big data technologies. CSEE J Power Energy Syst 1(3):59–67

    Article  Google Scholar 

  31. Zhao Z, Chen W, Wu X, Chen PC, Liu J (2017) Lstm network: a deep learning approach for short-term traffic forecast. IET Intell Transp Syst 11(2):68–75

    Article  Google Scholar 

Download references

Acknowledgments

This paper is supported by the National Key Research and Development Program of China (2018YFB1402802).

Author information

Authors and Affiliations

  1. College of Computer Science, Zhejiang University of Technology, Hangzhou, China

    Bin Cao, Jiawei Wu, Longchun Cao & Jing Fan

  2. School of Computer Science and Technology, Xidian University, Xi’an, China

    Yueshen Xu

Authors
  1. Bin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiawei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Longchun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yueshen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Fan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, B., Wu, J., Cao, L. et al. Long-Term and Multi-Step Ahead Call Traffic Forecasting with Temporal Features Mining. Mobile Netw Appl 25, 701–712 (2020). https://doi.org/10.1007/s11036-019-01447-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01447-9

Keywords

Search

Navigation