Forecasting future consumption of coniferous wood in India: a quantitative approach

Over the last few years, Canada has been very successful in increasing its trade in wood products with China. India however, still remains an elusive market. There is a large amount of peer reviewed literature on the specifics of the Indian wood market, and the potential for trade in softwood products. Whereas the majority of studies describe in great detail the opportunities and constraints in dealing with India, very little quantitative information is available about the trends and patterns that determine the Indian wood market. This study uncovered and described one such trend by identifying the relationships between the level of imports of softwood products and such factors as India's Gross Domestic Product (GDP), domestic production, the price of lumber on international markets, tariffs, and the price of Teak logs as a substitute for softwood products. This study analyzed 13 years of quarterly data using the ordinary least square regression technique. Diagnostics were conducted using Akaike and Schwartz criterions, the Durbin-Watson test, and the Breusch-Pagan-Godfrey test for heteroscedasticity. Results suggest that the indicated variable collectively explain 74% of variability in import levels. Two variables in particular, real GDP and the price of Teak have a significant, positive impact on the level of imports of softwood products with 0.45 and 0.49 as respective elasticities. Continuing growth of India's GDP will ensure an ever increasing demand for imported wood products in the years to come. To maximize this opportunity, North American exporters should not compete with New Zealand's low quality pine, but should instead focus on competing with dark coloured tropical hardwoods that are becoming prohibitively expensive as world wide supplies of Teak and other tropical hardwoods continue to diminish. --P. i.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b180562

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio