Publications by Tora Mullings
DATA 624: HW 9
library(mlbench) library(randomForest) library(caret) library(party) library(gbm) library(Cubist) 8.1 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" model1 <- randomForest(y~., data=simulated, ...
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DATA 624: HW 8
library(mlbench) library(nnet) library(kernlab) library(e1071) library(earth) library(caret) 7.2 set.seed(200) trainingData <- mlbench.friedman1(200, sd=1) trainingData$x <- data.frame(trainingData$x) featurePlot(trainingData$x, trainingData$y) testData <- mlbench.friedman1(5000, sd=1) testData$x <- data.frame(testData$x) knnModel <- tr...
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DATA 624 HW 7: Linear Regression & Its Cousins
6.2 library(AppliedPredictiveModeling) library(pls) library(caret) library(tidyverse) library(caTools) library(imputeR) library(RANN) library(corrplot) data(permeability) str(permeability) ## num [1:165, 1] 12.52 1.12 19.41 1.73 1.68 ... ## - attr(*, "dimnames")=List of 2 ## ..$ : chr [1:165] "1" "2" "3" "4" ... ## ..$ : chr "perm...
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DATA 624: Project 1
Part A – ATM Forecast, ATM624Data.xlsx library(tidyverse) library(fpp3) atm_data <- readxl::read_xlsx("ATM624Data.xlsx") %>% mutate(DATE = as_date(DATE, origin="1900-01-01")) %>% as_tsibble(index=DATE, key=c("ATM")) atm_data <- atm_data[complete.cases(atm_data), ] atm_data %>% autoplot(Cash) ggplot(data=atm_data, mapping=aes(DATE, Cas...
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DATA 624: HW 6
library(fpp3) library(urca) library(gridExtra) 9.1, 9.2, 9.3, 9.5, 9.6, 9.7, 9.8 9.1 All the lines are within the blue dotted lines, so all three plots show white noise. From the Hyndman book, The dashed blue lines indicate whether the correlations are significantly different from zero. For a white noise series, we expect 95% of the spikes ...
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DATA 624: HW5
Hyndman: 8.1, 8.5, 8.6, 8.7, 8.8, 8.9 library(fpp3) 8.1 Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of α and ℓ0, and generate forecasts for the next four months. pigs <- aus_li...
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DATA 624: HW4
Predictor Distributions, Missingness, Imputation library(mlbench) library(tidyverse) library(corrplot) library(reshape2) # for melt library(fpp3) 3.1 data(Glass) str(Glass) ## 'data.frame': 214 obs. of 10 variables: ## $ RI : num 1.52 1.52 1.52 1.52 1.52 ... ## $ Na : num 13.6 13.9 13.5 13.2 13.3 ... ## $ Mg : num 4.49 3.6 3.5...
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DATA 624: HW3
HW 3 5.1, 5.2, 5.3, 5.4 and 5.7 library(fpp3) 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: Australian Population (global_economy) Bricks (aus_production) NSW Lambs (aus_livestock) Household wealth (hh_budget). Australian takeaway food turnover (aus_...
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DATA 624: HW2
library(fpp3) Exercises: 3.1, 3.2, 3.3, 3.4, 3.5, 3.7, 3.8 and 3.9 3.1 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? global_economy ## # A tsibble: 15,150 x 9 [1Y] ## # Key: Country [263] ## Country C...
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DATA 624: HW1
2.1 2.2 2.3 2.4 2.5 2.8 library(fpp3) 2.1 Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec. Use ? (or help()) to find out about the data in each series. What is the time interval of each series? Use autoplot() to produce a time plot of each series. For the last plo...
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