Which of the following are examples of time series data?
The vital statistics of a cancer patient taken right before a major surgery
The monthly expenses of a small company recorded over a period of five years
Daily temperature, rainfall, humidity, and wind speeds measured at a particular location over a few months
Student final grades in all sections of a course at a university
2.
Consider the graph in the figure as shown below.
a.
Is the trend curve generally increasing, decreasing, or staying level over the entire time period?
b.
The data set USATemps1961-2023.csv contains the time series from which the graph was created. Find the moving average trendline with period 10 for the data.
Figure 5.20
3.
a.
What are the characteristics of white noise? Why is it important that the residuals of a time series model be white noise?
b.
Determine which of the following graphs most likely represents white noise.
4.
Four time series models were produced to model a given dataset. Various measures of error were run on each of the models, the results of which are shown in the table.
MAE
RMSE
MAPE
Model 1
103.1
131.2
0.097
Model 2
251.7
207.9
0.136
Model 3
110.7
129.0
0.115
Model 4
89.8
125.3
0.191
Table 5.8Measures of Error
a.
Identify the most accurate model based only on:
MAE
RMSE
MAPE
b.
Which model is likely to produce the least accurate forecasts overall, and why?
c.
Which model is likely to produce the most accurate forecasts overall, and why?
Reuse and redistribution of this content in digital or print format:
This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's prior written permission.
This book uses the
Creative Commons Attribution-NonCommercial-ShareAlike License, which means that you can reuse and modify the material only for noncommercial purposes, must attribute OpenStax, and must distribute any derivative works under the same license.
Any commercial printing of this textbook, including using a local or custom printer, must be approved by OpenStax, and proper citation provided.
OpenStax-copyrighted images, activities, assessments, and similar components of this book are subject to the same licensing – CC-BY-NC-SA. They can be used for noncommercial purposes with attribution. Commercial use requires permission.
Permission requests: Anyone who intends to incorporate this content (including text, images, and other components) into large language models, use it in AI offerings, use it commercially (including in print), and/or has questions about another use case is welcome to complete our
reuse request form.
Attribution information
If you are redistributing all or part of this book in a noncommercial print format,
then you must include on every physical page the following attribution:
Access for free at https://openstax.org/books/principles-data-science/pages/1-introduction
If you are redistributing all or part of this book in a noncommercial digital format,
then for every page that includes OpenStax content, you must license the derivative work
under the same CC-BY-NC-SA license as the original, and include on every digital page
view the following attribution:
The information below includes the information needed to generate citations in most
major styles (APA, MLA, etc.); you must reformat and organize the information as needed
to fit the requirements of the style. Use the information below to generate a citation.
We recommend using a citation tool such as
this one.
Authors: Dr. Shaun V. Ault, Dr. Soohyun Nam Liao, Larry Musolino
