Discussion Time Series Modeling.docx
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This document discusses time series modeling and decomposition. It explains that a time series can be decomposed into 4 components: trend, cycle, seasonal, and irregular. An additive model is used when components do not affect each other, while a multiplicative model is used when components do affect each other. The document also provides US federal debt data from 1945 to 2000 and asks the reader to analyze it by plotting a scatter plot, fitting linear and exponential trend lines, and interpreting which model better fits the data based on the r-squared values.
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Discussion Time Series Modeling.docx
- 1. Discussion – Time Series Modeling Time series are particularly useful to track variables such as revenues, costs, and profits over time. Time series models help evaluate performance and make predictions. Consider the following and respond in a minimum of 175 words:Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle (C), seasonal (S), and irregular (I). What is the difference between these components?The model can be additive or multiplicative. When we do use an additive model? When do we use a multiplicative model?The following list gives the gross federal debt(in millions of dollars) for the U.S. every 5 years from 1945 to 2000:Year Gross Federal Debt ($millions)1945 260,1231950 256,8531955 274,3661960 290,5251965 322,31819 70 380,9211975 541,9251980 909,0501985 1,817,5211990 3,206,5641995 4,921, 0052000 5,686,338Construct a scatter plot with this data. Do you observe a trend? If so, what type of trend do you observe?Use Excel to fit a linear trend and an exponential trend into the data. Display the models and their respective r^2.Interpret both models. Which model seems to be more appropriate? Why?