An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset

Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series data sets containing both isolated and assembled outliers. In this paper, we propose an Outlier Detection method based on Cross-correlation Analysis (ODCA). ODCA consists of three key parts. They are data preprocessing, outlier analysis, and outlier rank. First, we investigate a linear interpolation method to convert assembled outliers into isolated ones. Second, a detection mechanism based on the cross-correlation analysis is proposed for translating the high-dimensional data sets into 1-D cross-correlation function, according to which the isolated outlier is determined. Finally, a multilevel Otsu\u27s method is adopted to help us select the rank thresholds adaptively and output the abnormal samples at different levels. To illustrate the effectiveness of the ODCA algorithm, four experiments are performed using several high-dimensional time series data sets, which include two smallscale sets and two large-scale sets. Furthermore, we compare the proposed algorithm with the detection methods based on wavelet analysis, bilateral filtering, particle swarm optimization, auto-regression, and extreme learning machine. In addition, we discuss the robustness of the ODCA algorithm. The statistical results show that the ODCA algorithm is much better than existing mainstream methods in both effectiveness and time complexity

The shocklet transform: a decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series

We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series—termed the Discrete Shocklet Transform (DST)—and an associated similarity search routine, the Shocklet Transform And Ranking (STAR) algorithm, that indicates time windows during which panels of time series display qualitatively-similar anomalous behavior. After distinguishing our algorithms from other methods used in anomaly detection and time series similarity search, such as the matrix profile, seasonal-hybrid ESD, and discrete wavelet transform-based procedures, we demonstrate the DST’s ability to identify mechanism-driven dynamics at a wide range of timescales and its relative insensitivity to functional parameterization. As an application, we analyze a sociotechnical data source (usage frequencies for a subset of words on Twitter) and highlight our algorithms’ utility by using them to extract both a typology of mechanistic local dynamics and a data-driven narrative of socially-important events as perceived by English-language Twitter

Evaluating the impact of social-media on sales forecasting: a quantitative study of worlds biggest brands using Twitter, Facebook and Google Trends

In the world of digital communication, data from online sources such as social networks might provide additional information about changing consumer interest and significantly improve the accuracy of forecasting models. In this thesis I investigate whether information from Twitter, Facebook and Google Trends have the ability to improve daily sales forecasts for companies with respect to the forecasts from transactional sales data only. My original contribution to this domain, exposed in the present thesis, consists in the following main steps: 1. Data collection. I collected Twitter, Facebook and Google Trends data for the period May 2013 May 2015 for 75 brands. Historical transactional sales data was supplied by Certona Corporation. 2. Sentiment analysis. I introduced a new sentiment classification approach based on combining the two standard techniques (lexicon-based and machine learning based). The proposed method outperforms the state-of-the-art approach by 7% in F-score. 3. Identification and classification of events. I proposed a framework for events detection and a robust method for clustering Twitter events into different types based on the shape of the Twitter volume and sentiment peaks. This approach allows to capture the varying dynamics of information propagation through the social network. I provide empirical evidence that it is possible to identify types of Twitter events that have significant power to predict spikes in sales. 4. Forecasting next day sales. I explored linear, non-linear and cointegrating relationships between sales and social-media variables for 18 brands and showed that social-media variables can improve daily sales forecasts for the majority of brands by capturing factors, such as consumer sentiment and brand perception. Moreover, I identified that social-media data without sales information, can be used to predict sales direction with the accuracy of 63%. The experts from the industry consider the results obtained in this thesis to be valuable and useful for decision making and for making strategic planning for the future

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Money, policy regimes and economic fluctuations.

Part I deals with the estimation of money demand functions. Several non-structural interpretations of the conventionally estimated functions are surveyed and discussed (Chapter 1). An application to Italian data is then presented, focusing on two such interpretations. First (Chapter 2), the role of expectations in determining money demand behaviour is assessed. Since monetary policy regimes have a direct effect on the time-series properties of interest rates, the identification of clear regime changes may provide a powerful test of forward-looking models of money demand. An expectations model is constructed, which is stable in the face of the Italian monetary policy regime change in 1970, when traditional backward-looking money demand functions show remarkable instability. Second (Chapter 3), the existence of multiple long-run relations among the variables relevant to money demand is shown to create problems for the interpretation of single-equation estimates. To obtain a satisfactory specification of the long-run relations and the short-run dynamics of the system around equilibrium, a sequential procedure is devised and applied. In Part II, the controversy between "real" and "monetary" theories of fluctuations is examined (Chapter 4). A "monetary" equilibrium model of the cycle is constructed, extending the original Lucas "island" framework to allow for a powerful role for stabilization policy. The implications of alternative monetary policy regimes are derived and tested on U.S. data, comparing two periods (1922-1940 and 1952-1968) with a different policy stance. Chapter 5 investigates the relative importance of the "money" and "credit" channels of monetary transmission for Italy in the 1982-1994 period, using a structural VAR methodology. Monetary policy is effective, though not through a "credit channel", and independent disturbances to credit supply have sizeable real effects. In Chapter 6 the focus is shifted to anticipated fiscal policy actions and their effect on consumption. A long series of pre-announced income tax changes is examined for the U.K.. Consumption reacts to such fiscally-induced disposable income changes only at the implementation dates

The Financial Economics of White Precious Metals - A Survey

This article provides a review of the academic literature on the financial economics of silver, platinum and palladium. The survey covers the findings on a wide variety of topics relation to the White Precious Metals including Market Efficiency, Forecastability, Behavioral Findings, Diversification Benefits, Volatility Drivers, Macroeconomic Determinants, and their relationships with other assets

Bitcoin : users’ characteristics, motivations and investment behaviours

In less than a decade, the cryptocurrency known as Bitcoin has gone from a fringe phenomenon to a topic of increasing interest to academia and mainstream investors. However, despite the growing body of research seeking to understand Bitcoin, the pseudonymous, decentralised, and globally-diffused nature of its user base means that the individuals who use it remain poorly understood. In particular, the motivations, risk-appreciation, and investment behaviours of early adopters and innovators are subject to supposition in the absence of data derived from the user base. This thesis seeks to address this gap in knowledge by employing a multi-stage, mixed methodology approach and a theoretical framework to understand the Bitcoin user base. Utilising semantic analysis, a survey of online cryptocurrency communities, and econometric time-series analysis, this thesis addresses the extent and nature of Bitcoin in hedging; how individual users perceive their own motivations, uses, and risks that have driven their behaviour; and the nature of the relationship between the prices of cryptocurrency and indices of confidence. Analysis of the data determined that the use of Bitcoin as an instrument of hedging is limited, and influenced by political and institutional factors. Likewise, its motivations, uses, and risks are reflective of the users’ political ideology, with the community and marketplace becoming more sophisticated as they evolve over time. Additionally, despite several case studies demonstrating risk-averse adoption of Bitcoin, there is no relationship between its prices and confidence.Doctor of Philosoph