Izvestiya of Saratov University.

Economics. Management. Law

ISSN 1994-2540 (Print)
ISSN 2542-1956 (Online)


For citation:

Nosov V. V., Tsypin A. P. Study of Rail Transport on the Basis of Historical Time Series . Journal Izvestiya of Saratov University. Economics. Management. Law, 2015, vol. 15, iss. 1, pp. 81-85. DOI: 10.18500/1994-2540-2015-15-1-81-85

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Russian
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Article
UDC: 
656.025:311.17

Study of Rail Transport on the Basis of Historical Time Series

Autors: 
Nosov Vladimir Vladimirovich, Russian State Social University
Tsypin Alexander P., Moscow State University of Food Production
Abstract: 

Introduction. Significant territorial space of Russia Federation determined the special place of rail transport for the economic development of the country, so the bulk of the goods in the country is transported by rail. An important role in assessing the condition and prospects of development of rail transport plays an analysis of the historical time series of indicators of passenger and freight traffic. Methods. In this paper, using statistical and econometric methods analyzed historical time series of indicators of passenger and freight railway transport for the period from 1956 to 2012 and presents a forecast for the period up to 2015. Results. The analysis showed the absence of a general trend in the historical time series of indicators of passenger and freight traffic. In such cases, to describe the trends may not apply analytical alignment and should use self-correcting recurrent patterns that characterize the time-varying, dynamic properties of a number of speakers, take into account the value of the previous levels, and provide an opportunity to get a fairly accurate predictions of future levels. Under the influence of the prevailing socio-economic conditions, the dynamics of the performance of rail transport in the forthcoming period will have moved in different directions: turnover in the forecast period is increase and pass-turnover is decrease.

Reference: 
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  2. Brown G. R. Smoothing, Forecasting and Prediction of Discrete Time Series. New York, Dover Phoenix Editions, 2004. 468 р.
  3. Hyndman R. J., Koehler A. B., Ord J. K., Snyder R. D. Forecasting with Exponential Smoothing. The State Space Approach. Springe 2008. XII. 360 p.
Received: 
17.11.2014
Accepted: 
15.12.2014