A multi-model methodology for forecasting sales and returns of liquefied petroleum gas cylinders
Author:
Funder
COST
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
http://link.springer.com/content/pdf/10.1007/s00521-020-04713-0.pdf
Reference40 articles.
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4. Balestra P, Nerlove M (1966) Pooling cross section and time series data in the estimation of a dynamic model: the demand for natural gas. Econometrica 34(3):585–612
5. Brockwell PJ, Davis RA (1991) Time series: theory and methods, 2nd edn. Springer, Berlin
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