Mining Weighted Frequent Closed Episodes over Multiple Sequences

Frequent episode discovery is introduced to mine useful and interesting temporal patterns from sequential data. The existing episode mining methods mainly focused on mining from a single long sequence consisting of events with time constraints. However, there can be multiple sequences of different importance as the persons or entities associated with each sequence can be of different importance. Aiming to mine episodes in multiple sequences of different importance, we first define a new kind of episodes, i.e., the weighted frequent closed episodes, to take sequence importance, episode distribution and occurrence frequency into account together. Secondly, to facilitate the mining of such new episodes, we present a new concept called maximal duration serial episodes to cut a whole sequence into multiple maximum episodes using duration constraints, and discuss its properties for episode shrinking processing. Finally, based on the theoretical properties, we propose a two-phase approach to efficiently mine these new episodes. In Phase I, we adopt a level-wise episode shrinking framework to discover the candidate frequent closed episodes with the same prefixes, and in Phase II, we match the candidates with different prefixes to find the frequent close episodes. Experiments on simulated and real datasets demonstrate that the proposed episode mining strategy has good mining effectiveness and efficiency

Farming smarter, not harder: securing our agricultural economy

In the context of rising global demand, resource scarcity, and environmental pressures, this report considers the future of Australian agriculture. Global populations are growing and food prices are skyrocketing. This creates new market opportunities for Australian agriculture. But Australia has fragile and vulnerable soils, which are being degraded at an unsustainable rate. If we continue with ‘business as usual’, we will keep losing soils faster than they can be replaced. Acting now to improve soil condition could increase agricultural production by up to 2.1 billion per year. It could also help farmers cut costs on fertiliser and water use. “Winners of the food boom will be countries with less fossil fuel intensive agriculture, more reliable production, and access to healthy land and soils” said the report’s lead author Laura Eadie. “How we manage our land and soils will be key to whether Australia sees more of the upsides or downsides of rising global food demand.” Farming Smarter, Not Harder finds that Australian agriculture can build a lasting competitive advantage through innovation that raises agricultural productivity, reduces fuel and fertiliser dependence, and preserves the environment and resources it draws on. To achieve this, Australia needs to: Invest in knowledge: increase government investment in research and development by up to 7% a year; increase funding for extension programs; implement the Productivity Commission’s recommendation to set up Rural Research Australia; fund the national soil health strategy with an endowment sufficient to support ongoing research and monitoring for at least 20 years. Stop chopping and changing support for regional natural resource management: Federal and State governments should commit to a 10-year agreement to provide stable longterm funding for regional Natural Resource Management (NRM) bodies, including specific funding to monitor long-term trends in natural resource condition. Enable accountable community governance of land and soil management: To enable farming communities to protect themselves from free-riding, they should be supported to develop stewardship standards based on a shared understanding of what it takes to maintain productive agricultural landscapes over the long term. Align financial incentives with the long-term needs of sustainable farming communities: In addition to the drought policy reforms announced on October 26, drought assistance policies should support farming communities to take a lead in preparations for more frequent and severe droughts, and should be linked to community stewardship standards. “Recent projections indicate the potential doubling of exports by 2050, according to the National Food Plan and ANZ-commissioned Greener Pastures report. Our work looks at how to support farmers dealing with the practical challenges of seizing this opportunity, in the context of soil degradation and rising input costs”, said Laura Eadie. The case to increase research funding and foster innovative farming is made even stronger by the likely impacts of climate change. Without action to adapt to more variable and extreme weather, by 2050 Australia could lose 6.5 billion per year in wheat, beef, mutton, lamb and dairy production. The report profiles leading farmers who are already seeing the benefits of innovations in sustainable farming. It proposes simple measures to support them and the agricultural communities that depend on healthy farming landscapes. Download Farming Smarter, Not Harder report in full [Australia\u27s newly appointed Advocate for Soil Health, Michael Jeffery, also chairs the non-profit organisation Soils for Life which is already actively encouraging wider adoption of smarter farming. The Soils for Life report Innovations for Regenerative Landscape Management showcases a range of case studies of these farming innovations in practise, and the positive economic, environmental and social outcomes they are achieving. Read the case studies, learn more about the challenges landscape degradation will bring and what we can do about it at www.soilsforlife.org.au.

What is a Business Cycle?

This paper considers the question in its title from several angles. Part 1 looks at economic history and the development of thinking about business cycles - the popular meaning and economists' definitions and ideas. Part 2 reviews the lessons from business cycle chronologies and duration data, the concepts of periodicity of cycles and phases, and the apparent moderation of macroeconomic fluctuations in the second half of the 20th century. Part 3 compares the recent business cycles and growth cycles for several major industrialized, market-oriented countries. Part 4 discusses the role of endogenous cyclical variables, the outside shocks of various types, the systematic timing sequences, and the regularities of cyclical comovements and amplitudes. Understanding business cycles is aided by each of these models of analysis. Business cycles have varied greatly over the past 200 years in length, spread, and size. At the same time, they are distinguished by their recurrence, persistence, and pervasiveness. They make up a class of varied, complex, and evolving phenomena of both history and economic dynamics. Theories or models that try to reduce them to a single causal mechanism or shock are unlikely to succeed.

Sentiment Analysis of COVID-19 Pandemic on the Stock Market

COVID-19 is a dreadful infectious disease, morphed into an economic crisis causing extensive and longstanding ramifications across global markets. Investors continue to hear about COVID-19 and its impact in one corner of the globe or the other for a long time. Though the effects of COVID19 started in December 2019 in Wuhan, China, global markets did not respond actively till W.H.O officially declared on March 11, 2020, that the COVID19 outbreak is a global pandemic. These multi-channel events have eroded investor sentiment, tanking the global stock markets. This article uses a machine learning approach to Twitter to analyze and follow investor sentiment that has guided the market to the new low during the first 150 days of the COVID-19 era. The only respite for recovery of financial markets is the lowering of COVID-19 infected cases for the time being till a vaccine is developed for the virus

Commodity Speculation and Commodity Investment

I distinguish between speculation and index-based investment in commodity futures stressing the differing motivations of the two groups and the differing instruments that they use. I discuss the amounts of money deployed in these activities. I document evidence of extrapolative behaviour in metals prices, consistent with speculation affecting prices, and show that in at least one market (soybeans) index-based investment has a significant and persistent price impact.Commodities, Speculation, Asset Allocation

Text-mining in macroeconomics: the wealth of words

The coming to life of the Royal Society in 1660 surely represented an important milestone in the history of science, not least in Economics. Yet, its founding motto, ``Nullius in verba'', could be somewhat misleading. Words in fact may play an important role in Economics. In order to extract relevant information that words provide, this thesis relies on state-of-the-art methods from the information retrieval and computer science communities. Chapter 1 shows how policy uncertainty indices can be constructed via unsupervised machine learning models. Using unsupervised algorithms proves useful in terms of the time and resources needed to compute these indices. The unsupervised machine learning algorithm, called Latent Dirichlet Allocation (LDA), allows obtaining the different themes in documents without any prior information about their context. Given that this algorithm is widely used throughout this thesis, this chapter offers a detailed while intuitive description of its underlying mechanics. Chapter 2 uses the LDA algorithm to categorize the political uncertainty embedded in the Scottish media. In particular, it models the uncertainty regarding Brexit and the Scottish referendum for independence. These referendum-related indices are compared with the Google search queries ``Scottish independence'' and ``Brexit'', showing strong similarities. The second part of the chapter examines the relationship of these indices on investment in a longitudinal panel dataset of 2,589 Scottish firms over the period 2008-2017. It presents evidence of greater sensitivity for firms that are financially constrained or whose investment is to a greater degree irreversible. Additionally, it is found that Scottish companies located on the border with England have a stronger negative correlation with Scottish political uncertainty than those operating in the rest of the country. Contrary to expectations, we notice that investment coming from manufacturing companies appears less sensitive to political uncertainty. Chapter 3 builds eight different policy-related uncertainty indicators for the four largest euro area countries using press-media in German, French, Italian and Spanish from January 2000 until May 2019. This is done in two steps. Firstly, a continuous bag of word model is used to obtain semantically similar words to ``economy'' and ``uncertainty'' across the four languages and contexts. This allows for the retrieval of all news-articles relevant to economic uncertainty. Secondly, LDA is again employed to model the different sources of uncertainty for each country, highlighting how easily LDA can adapt to different languages and contexts. Using a Bayesian Structural Vector Autoregressive set up (BSVAR) a strong heterogeneity in the relationship between uncertainty and investment in machinery and equipment is then documented. For example, while investment in France, Italy and Spain reacts heavily to political uncertainty shocks, in Germany it is more sensitive to trade uncertainty shocks. Finally, Chapter 4 analyses English language media from Europe, India and the United States, augmented by a sentiment analysis to study how different narratives concerning cryptocurrencies influence their prices. The time span ranges from April 2013 to December 2018 a period where cryptocurrency prices experienced a parabolic behaviour. In addition, this case study is motivated by Shiller's belief that narratives around cryptocurrencies might have led to this price behaviour. Nonetheless, the relationship between narratives and prices ought to be driven by complex interactions. For example, articles written in the media about a specific phenomenon will attract or detract new investors depending on their content and tone (sentiment). Moreover, the press might also react to price changes by increasing the coverage of a given topic. For this reason, a recent causal model, Convergent Cross Mapping (CCM), suited to discovering causal relationships in complex dynamical ecosystems is used. I find bidirectional causal relationships between narratives concerning investment and regulation while a mild unidirectional causal association exists in narratives that relate technology and security to prices