Abstract
Forecasting demand is challenging. Various products exhibit different demand patterns. While demand may be constant and regular for one product, it may be sporadic for another, as well as when demand occurs, it may fluctuate significantly. Forecasting errors are costly and result in obsolete inventory or unsatisfied demand. Methods from statistics, machine learning, and deep learning have been used to predict such demand patterns. Nevertheless, it is not clear for what demand pattern, which algorithm would achieve the best forecast. Therefore, even today a large number of models are used to forecast on a test period. The model with the best result on the test period is used for the actual forecast. This approach is computationally and time intensive and, in most cases, uneconomical. In our paper we show the possibility to use a machine learning classification algorithm, which predicts the best possible model based on the characteristics of a time series. The approach was developed and evaluated on a dataset from a B2B-technical-retailer. The machine learning classification algorithm achieves a mean ROC-AUC of 89%, which emphasizes the skill of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
This data subset is of a sufficient size to provide an indication of the validity of our approach.
References
Ivanov, D., Tsipoulanidis, A., Schönberger, J.: Global supply chain and operations management, vol. 2, Springer (2017) https://doi.org/10.1007/978-3-319-24217-0
Moon, M.: Demand and Supply Integration: The Key to World-Class Demand Forecasting. De Gruyter, Berlin (2018)
Kourentzes, N.: Intermittent demand forecasts with neural networks. Int. J. Prod. Econ. 143(1), 198–206 (2013). https://doi.org/10.1016/j.ijpe.2013.01.009
Syntetos, A., Boylan, J., Croston, J.: On the categorization of demand patterns. J. Oper. Res. Soc. 56(5), 495–503 (2005)
Kiefer, D., Ulmer, A.Dinther, C.V.: Application of Artificial Intelligence to optimize forecasting capability in procurement, pp. 69–80 (2019)
Bauer, M., van Dinther, C., Kiefer, D.: Machine learning in SME: an empirical study on enablers and success factors, pp. 1–15 (2020)
Gamberini, R., Lolli, F., Rimini, B., Sgarbossa, F., Cattani, C.: Forecasting of sporadic demand patterns with seasonality and trend components: an empirical comparison between holt-winters and (S) ARIMA methods. In: Mathematical Problems in Engineering: Hindawi Publishing Corporation, vol. 2010, pp. 1–14 (2010)
Pai, P., Lin, K., Lin, C., Chang, P.: Time series forecasting by a seasonal support vector regression model. Expert Syst. Appl. 37(6), 4261–4265 (2010)
Fu, W., Chien, C., Lin, Z.H.: A hybrid forecasting framework with neural network and time-series method for intermittent demand in semiconductor supply chain. In: Moon, I., Lee, G.M., Park, J., Kiritsis, D., von Cieminski, G. (eds.) APMS 2018. IAICT, vol. 536, pp. 65–72. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99707-0_9
Kiefer, D., Grimm, F., Bauer, M., Dinther, C. V.: Demand forecasting intermittent and lumpy time series: comparing statistical, machine learning and deep learning methods, pp. 1425–1434 (2021)
Nikolopoulos, K., “We need to talk about intermittent demand forecasting,” Eur. J. Oper. Res.: Elsevier, 2020.
Cheng, C.-Y., Chiang, K.-L., Chen, M.-Y.: Intermittent Demand Forecasting in a Tertiary Pediatric Intensive Care Unit. J. Med. Syst. 40(10), 1–12 (2016). https://doi.org/10.1007/s10916-016-0571-9
Bergmeir, C., Hyndman, R., Koo, B.: A note on the validity of cross-validation for evaluating autoregressive time series prediction. Comput. Stat. Data Anal. 120, 70–83 (2018)
Fürnkranz, J. et al.: Mean Absolute Error, p. 652 (2010)
Croston, J.D.: Forecasting and stock control for intermittent demands. Oper. Res. Q. (1970-1977) 23(3), 289 (1972). https://doi.org/10.2307/3007885
Winters, P.: Forecasting sales by exponentially weighted moving averages. Manag. Sci. 6(3), 324–342 (1960)
Holt, C.: Forecasting seasonals and trends by exponentially weighted moving averages. Int. J. Forecast. 20(1), 5–10 (2004)
Wang, Y., Guo, Y.: Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost. China Commun. 17(3), 205–221 (2020)
Altincöp, H., Oktay, A.: Air pollution forecasting with random forest time series Analysis, pp. 1–5 (2018)
Abbasimehr, H., Shabani, M., Yousefi, M.: An optimized model using LSTM network for demand forecasting. Comput. Ind. Eng. 143, 106435 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kiefer, D., Bauer, M., Grimm, F. (2021). Univariate Time Series Forecasting: Machine Learning Prediction of the Best Suitable Forecast Model Based on Time Series Characteristics. In: Zimmermann, A., Howlett, R.J., Jain, L.C., Schmidt, R. (eds) Human Centred Intelligent Systems . KES-HCIS 2021. Smart Innovation, Systems and Technologies, vol 244. Springer, Singapore. https://doi.org/10.1007/978-981-16-3264-8_15
Download citation
DOI: https://doi.org/10.1007/978-981-16-3264-8_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3263-1
Online ISBN: 978-981-16-3264-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)