Abstract:The maritime industry is one of those rare industries that are both highly international integrated to international trade and also highly capital intensive dependent on substantial investment amount. In the literature, ship investments have not been widely examined through the firm-level investment theories to explore the link between investment level and asset price valuation. The general trend in the literature of ship investments is to analyse the relationship among the shipping markets (newbuilding, second-hand, freight rate and scrap) and their impact on asset price valuation, the timing of investments and market entry and exit conditions. In this paper, we extensively reviewed the literature of firm-level investment theories and ship investments. We showed that the application of firm-level investment theories to the ship investments is confined to the basic investment valuation models, such as Net Present Value and Real Option Analysis. Ship investments need to be examined by firm-level investment theories to define firm/industry value maximization level within the approach of the solid investment theories.

A growing body of theoretical and empirical literature analyses the relationship between finance and economic growth. The relationship has been strongly supported by many empirical analyses. However, the 2008 Global Financial Crisis (GFC) and the significantly improved econometric techniques made scholars to revisit this relationship. The main motivation of this paper is to empirically revisit the relationship between financial development and economic growth, especially one under the effect of the world's greatest financial crisis since the Great Depression. In this study, both fixed effect and dynamic panel data analysis are conducted by using 147 countries over the period of 2000-2013. The analysis results prove the destructive effect of the GFC on the relationship between financial development and economic growth. Also, the finding showed that the effect of traditional financial development proxies has reduced after the crisis.

Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights can be obtained through mining temporal patterns from these time series. Unlike traditional pattern mining, temporal pattern mining (TPM) adds additional temporal aspect into extracted patterns, thus making them more expressive. However, adding the temporal dimension into patterns results in an exponential growth of the search space, significantly increasing the mining process complexity. Current TPM approaches either cannot scale to large datasets, or typically work on pre-processed event sequences rather than directly on time series. This paper presents our comprehensive Frequent Temporal Pattern Mining from Time Series (FTPMfTS) approach which provides the following contributions: (1) The end-to-end FTPMfTS process that directly takes time series as input and produces frequent temporal patterns as output.(2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures to enable fast computations for support and confidence. (3) A number of pruning techniques for HTPGM that yield significantly faster mining. (4) An approximate version of HTPGM which relies on mutual information to prune unpromising time series, and thus significantly reduce the search space. (5) An extensive experimental evaluation on real-world datasets from the energy and smart city domains which shows that HTPGM outperforms the baselines, consumes less memory and can scale to big datasets. The approximate HTPGM achieves up to 3 orders of magnitude speedup compared to the baselines while obtaining high accuracy compared to the exact HTPGM.

Very large time series are increasingly available from an ever wider range of IoT-enabled sensors, from which significant insights can be obtained through mining temporal patterns from them. A useful type of patterns found in many real-world applications exhibit periodic occurrences, and is thus called seasonal temporal patterns (STP). Compared to regular patterns, mining seasonal temporal patterns is more challenging since traditional measures such as support and confidence do not capture the seasonality characteristics. Further, the anti-monotonicity property does not hold for STPs, and thus, resulting in an exponential search space. This paper presents our Frequent Seasonal Temporal Pattern Mining from Time Series (FreqSTPfTS) solution providing: (1) The first solution for seasonal temporal pattern mining (STPM) from time series that can mine STP at different data granularities. (2) The STPM algorithm that uses efficient data structures and two pruning techniques to reduce the search space and speed up the mining process. (3) An approximate version of STPM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that STPM outperforms the baseline in runtime and memory consumption, and can scale to big datasets. The approximate STPM is up to an order of magnitude faster and less memory consuming than the baseline, while maintaining high accuracy.