Publications by Ethan Haley
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library(fpp3) ## ── Attaching packages ──────────────────────────────────────────── fpp3 0.4.0 ── ## ✓ tibble 3.1.5 ✓ tsibble 1.1.1 ## ✓ dplyr 1.0.7 ✓ tsibbledata 0.4.0 ## ✓ tidyr 1.1.4 ✓ feasts 0.2.2 #...
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Part A – ATM Forecast In part A, the task is to forecast how much cash is taken out of 4 different ATM machines for May 2010. The variable ‘Cash’ is provided in hundreds of dollars. The instructions are somewhat ambiguous on purpose to make this have a little more business feeling. Explain and demonstrate your process, techniques used and n...
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1) Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: Australian Population (global_economy) The population has increased at a steady rate, so the drift method is the best choice of the 3. ausPop = global_economy %>% filter(Country=="Australia") %>% select(Pop...
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library(fpp3) ## ── Attaching packages ──────────────────────────────────────────── fpp3 0.4.0 ── ## ✓ tibble 3.1.5 ✓ tsibble 1.1.1 ## ✓ dplyr 1.0.7 ✓ tsibbledata 0.4.0 ## ✓ tidyr 1.1.4 ✓ feasts 0.2.2 #...
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#install.packages("mlbench") library(mlbench) data(Glass) #str(Glass) UC Irvine ML Repo Glass ID Data R docs: “The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence (if it is correctly identified!).” 214 samples of glass, belonging to 6 ...
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library(glue) library(fpp3) ## ── Attaching packages ──────────────────────────────────────────── fpp3 0.4.0 ── ## ✓ tibble 3.1.5 ✓ tsibble 1.1.1 ## ✓ dplyr 1.0.7 ✓ tsibbledata 0.4.0 ## ✓ tidyr 1.1.4 ✓ feast...
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1) Figure 9.32 from https://otexts.com/fpp3/AR.html shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. a) Explain the differences among these figures. Do they all indicate that the data are white noise? They all indicate white noise, since the auto-correlations aren’t statistically significant, as evidenced by t...
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8.1. Recreate the simulated data from Exercise 7.2: library(mlbench) set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" (a) Fit a random forest model to all of the predictors, then estimate the variable importance ...
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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: (a) Start R and use these commands to load the data: library(dplyr) ## ## Attaching package: 'dplyr' ## The...
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Exercises from Chapter 7 of textbook Applied Predictive Modeling by Kuhn & Johnson Exercise 7.2 Friedman (1991) introduced several benchmark data sets create by simulation. One of these simulations used the following nonlinear equation to create data: \(y = 10 sin(\pi x_1x_2) + 20(x_3 − 0.5)^2 + 10x_4 + 5x_5 + N(0, \sigma^2)\) where the x val...
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