Publications by PK O’Flaherty
DATA624 Homework 7
Exercise 6.2 Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: Part a The matrix fingerprints contains the 1,107 binary molecular predictors for the 165 compounds, w...
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DATA624 Homework 4
Exercise 3.1 The UC Irvine Machine Learning Repository contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. Interestingly this was motivated b...
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DATA624 Project 1
Part A - ATM Forecast Forecast how much cash is taken out of 4 different ATM machines for May 2010. Data Here we process our data, develop an intuition for how to approach the forecast, and handling missing or anomalous data. Section Summary * Import * Visualize the data * Patterns * Problematic ATM3 * Remaining missing data * Replacing missing va...
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DATA624 Project 1
Part A - ATM Forecast Forecast how much cash is taken out of 4 different ATM machines for May 2010. Data Here we process our data, develop an intuition for how to approach the forecast, and handling missing or anomalous data. Section Summary * Import * Visualize the data * Patterns * Problematic ATM3 * Remaining missing data * Replacing missing va...
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DATA624 Homework 3
Exercise 5.1 Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: It seems if seasonality is stronger than trend then Seasonal Naïve works best and if trend is stronger than seasonality then Drift works best as a baseline comparison forecast. Part a Australian Popul...
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DATA624 Homework6
Exercise 9.1 Figure 9.32 (located here) shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Part a Explain the differences among these figures. Do they all indicate that the data are white noise? Since they are random numbers, they should be independent from each other, and so we would expect the Autocorrelation Func...
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DATA624 Homework 5
### Load Libraries library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 Exercise 8.1 Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. Part a Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of alpha and l_0, and gen...
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DATA624 Homework 3
## Warning: package 'fpp3' was built under R version 4.3.3 Exercise 5.1 Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: It seems if seasonality is stronger than trend then Seasonal Naïve works best and if trend is stronger than seasonality then Drift works best...
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DATA624 Homework 2
Exercise 3.7.1 Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? The USA had the highest GDP every year from 1960 to 2017 but the story changes when you look at GDP per Capita. Then the USA had the highest from 1960 to 196...
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DATA624 Homework 2
Exercise 3.7.1 Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? The USA had the highest GDP every year from 1960 to 2017 but the story changes when you look at GDP per Capita. Then the USA had the highest from 1960 to 196...
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