Publications by Giorgio Garziano
Financial time series forecasting – an easy approach
Financial time series analysis and their forecasting have an history of remarkable contributions. It is then quite hard for the beginner to get oriented and capitalize from reading such scientific literature as it requires a solid understanding of basic statistics, a detailed study of the ground basis of time series analysis tools and the knowled...
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Visualizing Tennis Grand Slam Winners Performances
Data visualization of sports historical results is one of the means by which champions strengths and weaknesses comparison can be outlined. In this tutorial, we show what plots flavors may help in champions performances comparison, timeline visualization, player-to-player and player-to-tournament relationships. We are going to use the Tennis Gran...
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Weather forecast with regression models – part 1
In this tutorial we are going to analyse a weather dataset to produce exploratory analysis and forecast reports based on regression models. We are going to take advantage of a public dataset which is part of the exercise datasets of the “Data Mining and Business Analytics with R” book (Wiley) written by Johannes Ledolter. In doing that, we ar...
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Weather forecast with regression models – part 2
In the first part, I introduced the weather dataset and outlining its exploratory analysis. In the second part of our tutorial, we are going to build multiple logistic regression models to predict weather forecast. Specifically, we intend to produce the following forecasts: tomorrow’s weather forecast at 9am of the current day tomorrow’s wea...
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Weather forecast with regression models – part 3
In the previous part of this tutorial, we build several models based on logistic regression. One aspect to be further considered is the decision threshold tuning that may help in reaching a better accuracy. We are going to show a procedure able to determine an optimal value for such purpose. ROC plots will be introduced as well. Decision Threshol...
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Weather forecast with regression models – part 4
Results so far obtained allow us to predict the RainTomorrow Yes/No variable. In the first part, we highlighted that such factor variable can be put in relationship with the Rainfall quantitative one by: all.equal(weather_data$Rainfall > 1, weather_data$RainToday == "Yes") As a consequence, we are able so far to predict if tomorrow rainfall sha...
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Structural Changes in Global Warming
In time series analysis, structural changes represent shocks impacting the evolution with time of the data generating process. That is relevant because one of the key assumptions of the Box-Jenkins methodology is that the structure of the data generating process does not change over time. How can structural changes be identified ? The strucchange...
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ARIMA models and Intervention Analysis
In my previous tutorial Structural Changes in Global Warming I introduced the strucchange package and some basic examples to date structural breaks in time series. In the present tutorial, I am going to show how dating structural changes (if any) and then Intervention Analysis can help in finding better ARIMA models. Dating structural changes con...
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Outliers Detection and Intervention Analysis
In my previous tutorial Arima Models and Intervention Analysis we took advantage of the strucchange package to identify and date time series level shifts structural changes. Based on that, we were able to define ARIMA models with improved AIC metrics. Furthermore, the attentive analysis of the ACF/PACF plots highlighted the presence of seasonal ...
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Tennis Grand Slam Tournaments Champions Basic Analysis
The present tutorial analyses the Tennis Grand Slam tournaments main results from the statistical point of view. Specifically, I try to answer the following questions: – How to fit the distribution of the Grand Slam tournaments number of victories across players? – How to compute the probability of having player’s victories greater than a ...
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