Publications by Warner Alexis
DATA 624 Project 1
Project 1 This project consists of 3 parts - two required and one bonus and is worth 15% of your grade. The project is due at 11:59 PM on Sunday Oct 25. I will accept late submissions with a penalty until the meetup after that when we review some projects. ATM Forecast The bar graph illustrates the total cash withdrawals (in hundreds of dollars...
14852 sym R (23023 sym/120 pcs) 35 img
DATA 622 Assignment 2
Exploratory analysis and essay The data is related to direct marketing campaigns conducted by a Portuguese banking institution. These campaigns were executed via phone calls, with multiple contacts often made to the same client to assess whether they would subscribe to a bank term deposit. The dataset used for this analysis is bank-full.csv, wh...
17151 sym Python (21433 sym/86 pcs) 8 img
DATA 622 Homework 6
ARIMA MODELS Excercise 9.1 Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Explain the differences among these figures. Do they all indicate that the data are white noise? Let’s go through the questions one by one based on Figure 9.32. 1a. Explain the differences among these figures. Do they all ...
10649 sym R (9946 sym/58 pcs) 26 img
DATA 624 Homework 5
Forecasting: Principles and Practice Exercise 8.1 Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. library(forecast) ## Registered S3 method overwritten by 'quantmod': ## method from ## as.zoo.data.frame zoo library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects a...
13951 sym R (15390 sym/88 pcs) 14 img
Data 622 EDA
Exploratory analysis and essay The data is related to direct marketing campaigns conducted by a Portuguese banking institution. These campaigns were executed via phone calls, with multiple contacts often made to the same client to assess whether they would subscribe to a bank term deposit. The dataset used for this analysis is bank-full.csv, wh...
12616 sym Python (16631 sym/50 pcs) 6 img
Data 624 Homework 4
Data Preprocessing Exercise 3.1 The UC Irvine Machine Learning Repository6 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. Using vi...
6305 sym R (12097 sym/70 pcs) 43 img
DATA 624 Homework 3
Homework 3 Excercise 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_retail). Australian Populatio...
8454 sym R (11353 sym/59 pcs) 19 img
Data 624 Homework 2 Updated fpp3
Time Series Decomposition Excercise 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? ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ## Attach...
6685 sym R (10019 sym/42 pcs) 20 img
DATA 624 Homework 2
The Forcaster Toolbox Excercise 3.1 For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance. usnetelec usgdp mcopper enplanements ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ## Attaching package: 'tsibble' ## The following...
4754 sym R (6579 sym/52 pcs) 25 img
Data 624 Homework 1
Problem 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 plot, modify the axis labels and...
6767 sym R (8181 sym/55 pcs) 25 img