Published in Towards Data Science·PinnedMember-onlyA Quick Guide to Beautiful Scatter Plots in PythonVisualizing worldwide Life Expectancy vs GPD per capita — So you already know some Python and matplotlib. Perhaps you are like me and really like sophisticated, beautiful and insightful plots. However, when you encounter some basic examples to replicate them yourself, as seen in this documentation page, you might see something like this: which generates the following plot:10 min read10 min read
Published in Analytics Vidhya·Mar 21Member-onlyComparing Topological Sort Loop-based and Recursive ImplementationsWith examples in Java and Python Topological Sort is an algorithm used to order the vertices of a directed graph in a way that satisfies its dependencies. It has numerous applications in computer science, including scheduling tasks, detecting deadlocks, and resolving dependencies between software modules. In this article, we’ll dive…Chatgpt6 min readChatgpt6 min read
Published in Towards Data Science·Apr 21, 2021Member-onlyThe Data Science TrilogyNumPy, Pandas and Matplotlib basics — So you are new to Python. Or perhaps you are already familiar with these libraries, but wanted to get a quick refresher. …Python11 min readPython11 min read
Published in Analytics Vidhya·Mar 26, 2021Member-onlyQuick GIT is all you NEEDOkay, so you have come here because you want to learn Git/Github. So instead of writing a 10 lines paragraph about it, let’s get to the point. I will keep updating this article in the future to make it as comprehensive but concise as possible.Computer Science16 min readComputer Science16 min read
Published in Analytics Vidhya·Mar 15, 2021Member-onlyA Complete Introduction To Time Series Analysis (with R):: Exogenous modelsWe have come pretty far into our analysis of univariate time series. So far, we have considered some sort of time-based stochastic process X_{t}, dependent on current and previous noise and itself. However, what happens when in addition, we would like to model our endogenous process as also being dependent…9 min read9 min read
Published in Analytics Vidhya·Feb 27, 2021Member-onlyA Complete Introduction To Time Series Analysis (with R):: SARIMA modelsIn the last article, we saw one important useful extension to the ARMA models: the Autoregressive Integrated Moving Average or ARIMA models, which formalize and integrate the differencing factor into the model. This time, we will see yet another very useful extension: seasonal component with the SARIMA models. …Forecasting8 min readForecasting8 min read
Published in Analytics Vidhya·Feb 22, 2021Member-onlyA Complete Introduction To Time Series Analysis (with R):: ARIMA modelsIn the last section, we discussed model selection for ARMA(p,q) models by using the AIC, AICc, BIC, which are metric functions based on the likelihood and the parameters, providing a certain measure that can be used to compare models against each other on the same data. In this article, we…Machine Learning8 min readMachine Learning8 min read
Published in Analytics Vidhya·Jan 30, 2021Member-onlyA Complete Introduction To Time Series Analysis (with R):: Model Selection for ARMA(p,q)In the last section, we learned about Gaussian Time Series, a powerful and flexible assumption when it comes to ARMA(p,q) parameters estimation. In this article, we will see how we can select the “best” model among a number of fitted models. Maximum Likelihood Model Selection In the previous section, we saw how Gaussian assumptions…Statistics7 min readStatistics7 min read
Published in Analytics Vidhya·Jan 23, 2021Member-onlyA Complete Introduction To Time Series Analysis (with R):: Gaussian Time SeriesIn the last two articles, we saw a number of methods to independently estimate AR(p) and MA(q) coefficients, namely the Yule-Walker method, Burg’s Algorithm, and the Innovations Algorithm, as well as the Hannan-Risennan Algorithm, to jointly estimate ARMA(p,q) coefficients, by making use of initialized AR(p) and MA(q) coefficients with the…Machine Learning8 min readMachine Learning8 min read
Published in Analytics Vidhya·Jan 17, 2021Member-onlyA Complete Introduction To Time Series Analysis (with R):: Estimation of ARMA(p,q) Coefficients (Part II)In the last article, we learned about two algorithms to estimate the AR(p) process coefficients: the Yale-Walker equations method, and Burg’s algorithm. In this article, we will now see a very simple way to determine the MA(q) process coefficients, and a first approach to estimate the ARMA(p,q), jointly. …Statistics7 min readStatistics7 min read
