# How to learn R

There are an incredibly large number of resources available to you for learning R, Starting from the R Manual, step by step books, youtube videos, more books, R blogs and so on. Here you will find a list of only some of these resources: Where to get R (Software) You can download R form https://cran.r-project.org/. R-Studio: An R Editor with additional plus, and which provides … Continue reading How to learn R

# Article review: Generalized measures for the evaluation of Community Detection methods.

In this blog post, I will summarise an article that proposes a modified version of three community detection assessment measures (Purity, Adjusted Rand Index and Normalized Mutual Information). The modified measures include network topological information to assess misclassification errors according to nodes’ integration into the network. This article was published in 2013 in the International Journal of Social Network Mining by Vincent Labatut (Labatut, 2015). … Continue reading Article review: Generalized measures for the evaluation of Community Detection methods.

# Multivariate Time Series analysis with volatility-Oil Prices

With the basic analysis on the univariate time series on last blog post “Univariate Time Series Analysis -Oil Prices”. This blog post will continue the analysis on multivariate time series. First is using Henze-Zirklers test to check the multivariate normality. The mvnTest = ”hz” in the mvn function can perform the Henze-Zirklers test. The last column indicates whether data set follows a multivariate normality or … Continue reading Multivariate Time Series analysis with volatility-Oil Prices

# Univariate Time Series Analysis -Oil Prices

This blog post will try to modeling and forecasting univariate time series dataset with ARMA-GARCH model and exam the goodness of fit with some basic tests. The oil prices dataset is the log returns of four benchmarks(West Texas Intermediate (WTI), Brent Blend, Dubai Crude and Maya) from 10/1/1997 to 4/6/2010. In this data set, each benchmark contains 698 observations, each of them was divided between … Continue reading Univariate Time Series Analysis -Oil Prices

# Article review. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients.

In this post, I will summarise an article that proposes a redefinition of the clustering coefficients for two-mode networks. The new definition aims to solve some problems that arise from applying, in projected two-mode networks, the clustering coefficient defined in one-mode networks. This article was published in 2013 in the Journal ELSEVIER by Tore Opsahl (Opsahl, 2013). The author introduced the article by explaining some … Continue reading Article review. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients.

# Article review: The scales of human mobility.

In this blog post, I will summarise an article that proposes a new approach to model human mobility. This article was published in 2020 in the Journal Nature by Laura Alessandretti, Ulf Aslak and Sune Lehman (Alessandretti et al., 2020). The authors started the article by explaining that human mobility is a key issue to understand other phenomena such as people’s commuting flows, money’s … Continue reading Article review: The scales of human mobility.

# Article review. Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

This blog will review a survey of the applications of complex networks to real-world problems. In particular, six applications related to Social Networks, Economy and Security and Surveillance will be summarised. This article was published in the Journal Advances in Physics, in 2008 by Luciano da Fontoura Costa, Osvaldo N. Oliveira Jr., Gonzalo Travieso, Francisco Aparecido Rodrigues, Paulino Ribeiro Villas Boas, Lucas Antiqueira, Matheus Palhares … Continue reading Article review. Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

# Introduction to Graph Convolutional Network

Many important real-world datasets come in the form of graphs or networks: social networks, citation networks, protein-interaction networks, the World Wide Web, etc. The high interpretability of graph and the rise of deep learning has motivated to create a new intersection between deep learning and graph theory. When both these fields meet they create what we call geometric deep learning or graph neural network. It … Continue reading Introduction to Graph Convolutional Network

# Analysis of two-mode networks

In this blog post it will be analysed a two-mode network of students’ enrolments into modules at the University. Firstly, it will be shown how to visualise this two-mode network. Secondly, it will be demonstrated how to transform this network into a one-mode network to explore the similarities of each mode. The latter will be made using three methods: Overlaps count, Jaccard Similarity and Simple … Continue reading Analysis of two-mode networks

# Article review: Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using Machine Leaning techniques

Created in 2009, Bitcoin now is the most accepted cryptocurrency in the world and is traded on over 40 exchanges worldwide. Several innovative features of the Bitcoin such as decentralized peer-to-peer payment network without central banks, anonymity and greater accessibility relative to traditional currencies make it appealing to investors and traders. Thus, there is an increasing number of research that study the time series of … Continue reading Article review: Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using Machine Leaning techniques

# Article review: Modeling complex systems with adaptive networks

In this post, I will review an article that used adaptative networks to model complex systems in some real-world problems. This article was published in 2013 in the Journal ELSEVIER by Hiroki Sayama, Irene Pestov, Jeffrey Schmidt, Benjamin James Bush, Chun Wong, Junichi Yamanoi and Thilo Gross (Sayama et al., 2013). This article aimed to introduce fundamental concepts and properties of adaptive networks through a … Continue reading Article review: Modeling complex systems with adaptive networks

# Article review: Optimal forecast combination based on neural network for time series forecasting

Time series forecasting plays an important role in various practical applications ranging from energy, electrical load, tourism to finance. Improving forecasting performance is an important yet regularly difficult task. Forecast combination is considered as one effective way to improve the performance of forecasting. With the aim of utilizing Artificial Neural Network (ANN) to improve time series forecasting, the article titled “Optimal forecast combination based on … Continue reading Article review: Optimal forecast combination based on neural network for time series forecasting

# The robustness of ARIMA models with respect to parameter estimated and forecasted values

Aims, Objectives & Procedure followed My dissertation aimed in exploring how robust ARIMA (a time series forecasting technique) modelling is when time series data is non-stationary. What’s more, once robustness was explored an overarching framework was created as a supplementary aim.In particular this overarching framework consisted of a s set of rules on how to obtain accurate forecasts through ARIMA modelling when data exhibit certain … Continue reading The robustness of ARIMA models with respect to parameter estimated and forecasted values

# Exploring the performance of Neural Network Architectures in the forecast of stock prices

This article presents information about my MSc Dissertation at the University of Manchester. Key Features of the Dissertation The main objective of this dissertation was to investigate the performance of different type of Neural Network Architectures in the forecast of stock prices. The selected data for the research was about Microsoft’s stock price which consisted of the daily Open, Close, Low, High and Volume(OCLHV) and … Continue reading Exploring the performance of Neural Network Architectures in the forecast of stock prices

# Introduction to Convolutional Neural Networks

This is the first of two blogposts taking a look at the paper CNNPred: CNN-based stock market prediction using several data sources Ehsan Hoseinzade , Saman Haratizadeh , Faculty of New Sciences and Technologies, University of Tehran which attempts to showcase the application of CNNs to the stock market. In this first blogpost we are going to focus on the basics of convolutional neural networks, more specifically how they are applied … Continue reading Introduction to Convolutional Neural Networks

# A Gentle Introduction for Seasonal ARIMAX (SARIMAX)

Introduction When it comes to financial data, there is a high chance that seasonal patterns will be present there. These are defined as patterns that have cyclic behavior. Let’s assume, that there is a store that sells ice-cream during the whole year. An example of monthly seasonal patterns could be the increased ice-cream sales in that store during the summer period in comparison with the … Continue reading A Gentle Introduction for Seasonal ARIMAX (SARIMAX)

# Vector Autoregression (VAR) for house property sales time series

This blog post aims to generate forecasts of house property sales using Vector Autoregression (VAR) models. The dataset is downloaded from: https://www.kaggle.com/htagholdings/property-sales?select=ma_lga_12345.csv We utilize “ma_lga_12345.csv” dataset that contains data resampled using Median Price Moving Average (MA) in quarterly intervals. The data range from 30 September 2007 to 30 September 2019 at the time of download (8 July 2020). We focus on predicting the house price … Continue reading Vector Autoregression (VAR) for house property sales time series

# Forecast analysis with Random Forest for house property sales data.

In this blog post, I will perform a House Property Sales forecast using a Random Forest technique with a Linear Regression and a Time Series. To conduct these models, it was used two databases: The Raw data: 29580 observations of recorded sales data from 2007 to 2019. The MA data: 347 observations of Moving Average of Median Price grouped by quarterly intervals per property type … Continue reading Forecast analysis with Random Forest for house property sales data.

# Web Scraping – How to retrieve 1807 skills using three lines of code

This blog post will detail the steps required to begin your journey into web-scraping. Web Scraping can help solve many of the challenges that are faced in an ever-increasing digital word. Some of these challenges include being able to process the vast amount of data online, having a system that can react quickly to this data changing frequently, and making sure that the quality of the … Continue reading Web Scraping – How to retrieve 1807 skills using three lines of code

# Dataset: House Property Sales. Exploratory analysis.

By Maria Fernanda Ibarra Gutiérrez and Thu Trang Dinh In this blog post, we will describe the database about House Property Sales, which can be downloaded from: https://www.kaggle.com/htagholdings/property-sales?select=raw_sales.csv According to the first Figure, this database describes some characteristics of the property sales into 5 variables and 29,580 observations from the 7th of February 2007 to the 26 of July 2019. This database does not have … Continue reading Dataset: House Property Sales. Exploratory analysis.

# The impact of Covid-19 in World’s Economy

By Maria Fernanda Ibarra Gutiérrez The Coronavirus disease (Covid-19) is a worldwide health problem that according to the World Health Organization (WHO) has spread in 213 countries. Up to the 13th of April 2020, there were 1,807,308 cases around the world according to the Our World in Data database (Ritchie, 2020). At the current moment, the United States has the higher number of cases … Continue reading The impact of Covid-19 in World’s Economy