Forecasting Strategies Assignment | Online Assignment

Skill: The purpose of this assignment is to help you practice the following skills that are essential to your success in this course and in professional life beyond school: ∗ Formulate a problem statement related to a forecasting question. ∗ Design a strategy to answer the forecasting question. ∗ Collect, clean, and use publicly available time-series data. ∗ Evaluate various forecasting models to get the best possible forecast. ∗ Select the best model based on model selection criteria and accuracy measures. ∗ Execute appropriate statistical tests to obtain better forecasting accuracy. ∗ Interpret, communicate, and present forecast results in a report. Knowledge: This assignment will also help you to become familiar with the following valuable content knowledge in this discipline. ∗ Defining a forecasting problem. ∗ Utilizing a statistical program to compute, visualize, and analyze time-series data in economics, business, and the social sciences. ∗ Performing exploratory analysis. ∗ Selecting an appropriate statistical model among alternative models. ∗ Validating the selected statistical model. ∗ Interpreting models using parameters. ∗ Forecasting based on the selected statistical model. ∗ Assessing the accuracy of forecasts. ∗ Interpreting and communicate results effectively. Task: The final project will consist of a brief report between 10-12 double-spaced pages, including relevant tables and figures. To begin, you may choose your time-series dataset. Choose a dataset that has a time component and a variable to analyze over time. Choose a dataset that you would like to analyze according to the techniques that are outlined in the textbook and were discussed in class. Find the model that you believe fits your data best and build a forecast from this model. Assess the validity of that forecast. You must follow ‘the five basic steps in a forecasting task’ outlined in section 1.6 of your textbook. 1 Your report should be structured as follows: 1. Abstract (no more than 250 words): A summary of your basic findings. 2. Introduction (1-3 pages): A brief introduction/motivation to the problem at hand, relevant details about the data, additional relevant scientific information from searching the web, for example, and what is to be addressed. 3. Data Description (at most one page): A brief introduction to the data, data sources, and variable definitions. 4. Statistical Methods (1-2 pages): A discussion and justification of the methods you have used to analyze the data, and how you went about analyzing the data. Don’t forget to describe in some detail how and why the particular model was selected. 5. Results (2-3 pages): A presentation of the results of your analysis. Interpretations should include a discussion of statistical versus the practical importance of the results. 6. Discussion (1-2 pages): A synopsis of your findings and any limitations your study may suffer from. Present conclusions in terms that non-statisticians will understand. Quantitative and qualitative aspects should be discussed. Your report should be brief and to the point! It should be written in a language that is understandable to the scientific community. Criteria for Success: 1. Explanation: Excellent reports will use a data set a student is interested in and apply domain knowledge that describes why the forecasting is useful or important. This importance will be explained clearly in the introduction and conclusion of the report and will be evident to the reader. Reports should be clear and concise and use scientific language. 2. Process-oriented: Good reports will also follow the 5 basic steps in a forecasting task described in the textbook. Be sure to apply all the tools used and models discussed this semester to determine the best forecast, and clearly show why this forecast is the best using the accuracy measures. Reports should follow the task steps described above in the correct order. 3. Evidence-based: Use accuracy measures to back up the selected model. Show why alternative models are not as efficient with your specific dataset

FINAL PROJECT GUIDELINES
Purpose: The purpose of this assignment is for you to experience the complete life cycle of a
project based on time series data analysis. You will utilize the data analytic strategies that you
might use later in an analyst or business role. These strategies will be applied to a forecasting
problem that you are interested in answering. The forecasting steps require you to build a forecast
from the best t model and assess the accuracy of that forecast. For more information on the
forecasting project cycle, consult `the ve basic steps in a forecasting task’ outlined in section 1.6
(page 21-22) of your textbook.
Skill: The purpose of this assignment is to help you practice the following skills that are essential
to your success in this course and in professional life beyond school:
Formulate a problem statement related to a forecasting question.
Design a strategy to answer the forecasting question.
Collect, clean, and use publicly available time-series data.
Evaluate various forecasting models to get the best possible forecast.
Select the best model based on model selection criteria and accuracy measures.
Execute appropriate statistical tests to obtain better forecasting accuracy.
Interpret, communicate, and present forecast results in a report.
Knowledge: This assignment will also help you to become familiar with the following valuable
content knowledge in this discipline.
Dening a forecasting problem.
Utilizing a statistical program to compute, visualize, and analyze time-series data in eco-
nomics, business, and the social sciences.
Performing exploratory analysis.
Selecting an appropriate statistical model among alternative models.
Validating the selected statistical model.
Interpreting models using parameters.
Forecasting based on the selected statistical model.
Assessing the accuracy of forecasts.
Interpreting and communicate results eectively.
Task: The nal project will consist of a brief report between 10-12 double-spaced pages, including
relevant tables and gures. To begin, you may choose your time-series dataset. Choose a dataset
that has a time component and a variable to analyze over time. Choose a dataset that you would
like to analyze according to the techniques that are outlined in the textbook and were discussed
in class. Find the model that you believe ts your data best and build a forecast from this model.
Assess the validity of that forecast. You must follow `the ve basic steps in a forecasting task’
outlined in section 1.6 of your textbook.
1
Your report should be structured as follows:
1. Abstract (no more than 250 words): A summary of your basic ndings.
2. Introduction (1-3 pages): A brief introduction/motivation to the problem at hand, relevant
details about the data, additional relevant scientic information from searching the web, for
example, and what is to be addressed.
3. Data Description (at most one page): A brief introduction to the data, data sources, and
variable denitions.
4. Statistical Methods (1-2 pages): A discussion and justication of the methods you have used
to analyze the data, and how you went about analyzing the data. Don’t forget to describe in
some detail how and why the particular model was selected.
5. Results (2-3 pages): A presentation of the results of your analysis. Interpretations should
include a discussion of statistical versus the practical importance of the results.
6. Discussion (1-2 pages): A synopsis of your ndings and any limitations your study may suer
from. Present conclusions in terms that non-statisticians will understand. Quantitative and
qualitative aspects should be discussed.
Your report should be brief and to the point! It should be written in a language that is
understandable to the scientic community.
Criteria for Success:
1. Explanation: Excellent reports will use a data set a student is interested in and apply domain
knowledge that describes why the forecasting is useful or important. This importance will be
explained clearly in the introduction and conclusion of the report and will be evident to the
reader. Reports should be clear and concise and use scientic language.
2. Process-oriented: Good reports will also follow the 5 basic steps in a forecasting task described
in the textbook. Be sure to apply all the tools used and models discussed this semester to
determine the best forecast, and clearly show why this forecast is the best using the accuracy
measures. Reports should follow the task steps described above in the correct order.
3. Evidence-based: Use accuracy measures to back up the selected model. Show why alternative
models are not as ecient with your specic dataset.
2
Forecasting Final Project Rubric
Category Excellent Good Satisfactory Needs
Improvement
Interpretation
Ability to explain information
presented in mathematical forms
(e.g., equations, graphs,
diagrams, tables, words) in
analytics language
Effectively explains models that
were selected and why they
were selected using analytics
language
Attempts to
explain models,
does not fully
explain why they
were selected
using some
analytics language
Does not fully
explain what
models were
selected or why
they were selected.
Occasional analytics
language is used.
Does not state why
models were selected,
does not state why they
were selected. No
analytics language is
used.
Representation
Ability to convert relevant
information into various
mathematical forms (e.g.,
equations, graphs, diagrams,
tables, words)
Uses figures and equations
effectively to enhance
discussion
Uses figures and
equations to add
to discussion but
could be more
effective
Attempts to use
figures and
equations but does
not use them
effectively
Does not use figures or
equations in discussion.
Calculation
Ability to correctly deduce most
accurate model and use accuracy
measures
Correctly and comprehensively
uses models and accuracy
measures Calculations are also
presented elegantly (clearly,
concisely, etc.)
Models and
accuracy measures
attempted are
essentially all
successful and
sufficiently
comprehensive to
solve the problem.
Models and
accuracy measures
attempted are
either unsuccessful
or represent only a
portion of the
calculations
required to
comprehensively
solve the problem
Models and accuracy
measures are
attempted but are both
unsuccessful and are
not comprehensive.
Application / Analysis
Ability to make judgments and
draw appropriate conclusions
based on the quantitative analysis
of data, while recognizing the
limits of this analysis
Analysis is thorough and
complete, and coherent
throughout the discussion
Analysis is mostly
complete, does
not capture all
information
relevant to
discussion
Analysis is
incomplete, and
does not effectively
or coherently draw
conclusions
No analysis or analytics
thinking is present.
Assumptions
Ability to make and evaluate
important assumptions in
estimation, modeling, and data
analysis
Explicitly describes
assumptions of the unique
dataset and models and
provides compelling rationale
for why each assumption is
appropriate. Shows
awareness that confidence in
final conclusions is limited by
the accuracy of the
assumptions.
Explicitly
describes
assumptions and
provides
compelling
rationale for why
assumptions are
appropriate for
this dataset and
models.
Explicitly describes
assumptions for
this dataset and
models chosen.
Attempts to describe
assumptions for this
dataset and models
chosen.
Communication/Presentation
Expressing quantitative evidence
in support of the argument or
purpose of the work (in terms of
what evidence is used and how it
is formatted, presented, and
contextualized)
Uses quantitative information
in connection with the
argument or purpose of the
work, presents it in an
effective format, and
explicates it with consistently
high quality. Document is well
organized, error free, and
neat.
Uses quantitative
information in
connection with
the argument or
purpose of the
work, though
data may be
presented in a
less than
completely
effective format
or some parts of
the explication
may be uneven.
Uses quantitative
information but
does not
effectively connect
it to the argument
or purpose of the
work. Document
could be organized
better
Does not correctly
communicate
quantitative
information or does
not build a cleanly
presented final
document.