Title: Application of nonlinear stochastic single source of error state space models in the forecasting of mobile subscribers in India

Authors: Prabir Kumar Das; Anarghya Das

Addresses: Department of Quantitative Techniques, Indian Institute of Foreign Trade, Kolkata Campus, 1583 Madurdaha, Chowbaga Road, Kolkata, 700107, India ' Department of Computer Science and Engineering, School of Engineering and Applied Sciences, University at Buffalo, New York, 14261, USA

Abstract: The nonlinear stochastic single source of error state space model with error, trend, and seasonality (ETS) was employed and found appropriate for modelling mobile subscriber time series data for individual metro cities, total mobile subscribers in all metro cities, and subscribers in all of India using monthly data from March 1997 to December 2018. Out of the different ETS models, the multiplicative error, additive trend, and no seasonality (M, A, N) models were appropriate for all series. These models were compared to the autoregressive integrated moving average model. The final model was identified based on the DieboldMariano test and time series cross-validation. The performance of the final model was compared to the long short-term memory (LSTM) model. The mean absolute error and root mean squared error showed that the ETS (M, A, N) performed superior over the standard LSTM. The ETS (M, A, N) model was used for computing the point forecast and 95% confidence intervals of the forecast values for the next 24 months. The subscribers of Delhi, Mumbai, Kolkata, and India are projected, at 95% probability, to have a high of 100 million, 70 million, 60 million, and 2000 million subscribers, respectively, by December 2020.

Keywords: mobile subscribers; state space model; ETS; error; trend; and seasonality; LSTM; long short-term memory; time series cross-validation; nolinear; GSM.

DOI: 10.1504/IJDS.2020.115874

International Journal of Data Science, 2020 Vol.5 No.4, pp.333 - 357

Received: 06 May 2020
Accepted: 02 Feb 2021
Published online: 25 Jun 2021 *

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