Topological Data Analysis for Portfolio Management of Cryptocurrencies

Portfolio management is essential for any investment decision. Yet, traditional methods in the literature are ill-suited for the characteristics and dynamics of cryptocurrencies. This work presents a method to build an investment portfolio consisting of more than 1500 cryptocurrencies covering 6 years of market data. It is centred around Topological Data Analysis (TDA), a recent approach to analyze data sets from the perspective of their topological structure. This publication proposes a system combining persistence landscapes to identify suitable investment opportunities in cryptocurrencies. Using a novel and comprehensive data set of cryptocurrency prices, this research shows that the proposed system enables analysts to outperform a classic method from the literature without requiring any feature engineering or domain knowledge in TDA. This work thus introduces TDA-based portfolio management of cryptocurrencies as a viable tool for the practitioner

An industry case of large-scale demand forecasting of hierarchical components

Demand forecasting of hierarchical components is essential in manufacturing. However, its discussion in the machine-learning literature has been limited, and judgemental forecasts remain pervasive in the industry. Demand planners require easy-to-understand tools capable of delivering state-of-the-art results. This work presents an industry case of demand forecasting at one of the largest manufacturers of electronics in the world. It seeks to support practitioners with five contributions: (1) A benchmark of fourteen demand forecast methods applied to a relevant data set, (2) A data transformation technique yielding comparable results with state of the art, (3) An alternative to ARIMA based on matrix factorization, (4) A model selection technique based on topological data analysis for time series and (5) A novel data set. Organizations seeking to up-skill existing personnel and increase forecast accuracy will find value in this work

Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting

Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In this work, a system developed for a leading provider of cloud computing combining both user segmentation and demand forecasting is presented. It consists of a TDA-based clustering method for time series inspired by a popular managerial framework for customer segmentation and extended to the case of clusterwise regression using matrix factorization methods to forecast demand. Increasing customer loyalty and producing accurate forecasts remain active topics of discussion both for researchers and managers. Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level with significantly higher accuracy than a state of the art baseline. This work thus seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner

PARAFRASEO Y RESUMEN: IMPORTANCIA EN VETERINARIA Y AGRONOMÍA

El resumen y el parafraseo es una técnica de estudio que el estudiante universitario debe conocer, entender y aplicar para el desarrollo de trabajos de carácter académico en las diferentes asignaturas que deben aprobar durante la formación de tercer nivel o de postgrado.El trabajo se realizó con la revisión bibliográfica de artículos relacionados a la temática y se aplicó ejemplos de resúmenes y de parafraseo del área de agronomía y del sector pecuario para proporcionar herramientas a los estudiantes de la carrera de Medicina Veterinaria y Zootecnia y de Ingeniería Agronómica. Se estructuraron párrafos que ejemplifican las diferencias que existen entre el resumen que es representar la idea del autor en menor número de palabras y de parafraseo que se recomienda reescribir a partir del resumen, utilizando palabras propias de la persona que utiliza esta técnica pero siempre conservando el contexto. Es una técnica muy útil para el desarrollo de trabajos universitarios que requieren una gran concentración durante la lectura de diferentes textos