This tool finds the Granger causality relationship among the input time series and visualizes the results in a directed causal graph and a directed adjacency matrix. It applies the Lasso-Granger and Copula-Granger algorithms with length of lag l=1.
For more information, please see the following papers:
• Andrew Arnold, Yan Liu, and Naoki Abe. Temporal Causal Modeling with Graphical Granger Methods, KDD 2007.
• Taha Bahadori, Yan Liu. An Examination of Practical Granger Causality Inference, SDM 2013

In my most recent blog, I discussed the idea of aligning the supply of services to market demand. My conceptualization of ‘alignment’ specifically relates to time intervals: i.e. having people at the right place and at the right time – for example, to take advantage of opportunities – is a sign of alignment. Alignment for me is often about the relationship between capacity and incapacity: the ability to supply services versus the inability to satisfy the market demand for those services. In this blog I will be considering the interpretation of charts to evaluate the effectiveness of strategic allocation.

In this study, we predict the outcome of the football matches in the FIFA World Cup 2018 to be held in Russia this summer. We do this using classification models over a dataset of historic football results that includes attributes from the playing teams by rating them in attack, midfield, defence, aggression, pressure, chance creation and building ability. This last training data was a result of merging international matches results with AE games ratings of the teams considering the timeline of the matches with their respective statistics. Final predictions show the four countries with the most chances of getting to the semifinals as France, Brazil, Spain and Germany while giving Spain as the winner.

In the exercises below, we will explore more in Time Series analysis.The previous exercise is here,Please follow this in sequence.

We discuss a new approach for selecting features from a large set of features, in an unsupervised machine learning framework. In supervised learning such as linear regression or supervised clustering, it is possible to test the predicting power of a set of features (also called independent variables by statisticians, or predictors) using metrics such as goodness of fit with the response (the dependent variable), for instance using the R-squared coefficient. This makes the process of feature selection rather easy. Here this is not feasible. The context could be pure clustering, with no training sets available, for instance in a fraud detection problem. We are also dealing with discrete and continuous variables, possibly including dummy variables that represent categories, such as gender. We assume that no simple statistical model explains the data, so the framework here is model-free, data-driven. In this context, traditional methods are based on information theory metrics to determine which subset of features brings the largest amount of information.

Teaser: Tensor Networks can be seen as a higher-order generalization of traditional deep neural networks, and yet they lack an explicit non-linearity such as applying the ReLU or sigmoid function as we do with neural nets. A deeper understanding of what nonlinearity actually means, however, reveals that tensor networks can indeed learn non-linear functions. The non-linearity of tensor networks arises soley from the architecture and topology of the network itself.

In Data Science, we can use clustering analysis to gain some valuable insights from our data by seeing what groups the data points fall into when we apply a clustering algorithm. Today, we´re going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons!
• K-Means Clustering
• Mean-Shift Clustering
• Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
• Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
• Agglomerative Hierarchical Clustering

This is a tutorial for beginners interested in learning about MNIST and Softmax regression using machine learning (ML) and TensorFlow. When we start learning programming, the first thing we learned to do was to print ‘Hello World.’ It´s like Hello World, the entry point to programming, and MNIST, the starting point for machine learning.

At first glance, this reminds us of AI, when a machine decides how to manage a task based on statistical data. In fact, this concept is part of the AI phenomenon and makes it possible to develop machine intelligence and improve the decision-making process. According to NVidia, ‘Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world’. In a nutshell, a machine analyzes and recommends information without human participation. This work can be done manually but takes a plenty of time and effort. Thanks to huge computing power, modern machines perform data mining, data analytics and predictive modeling more effectively than people do. The next section will be dedicated to a recommender technique based on the machine learning approach.