Boston University Time Series in R Composition

This assignment provides practice in decomposing the seasonal time series data and then subtract that effect from the data and then to show how to address issues of correlations between successive values of the time series.

Before beginning this assignment, review the learning resources for this module, especially focus on Decomposing Time Series, Forecasting using Exponential Smoothing and ARIMA Models sections of A Little of R for Time Series by Avril Coghlan.

Complete the following steps and write a report to record your work, results and analysis.

You may use the attached data sets (average price data and NY benchmarked employment data) or one of time series data from data( ). Please note that some time series are more amenable to time series analysis than others.

It is important that you understand data structure and be able to appropriately transform/clean raw data prior to analysis. “Average price data.xlsx” is in the long format. The data series are stacked on top of each other. That is how the data is provided the Bureau of Labor Statistics. The names of variables are provided in the “series” worksheet. You need to pick one series name and use it to filter or subset the data prior to analysis. Some series are better than others. You may want to use only the portions of the series after 2009, since the Great Recession was an one-time event that cannot be modeled.

A. Decompose seasonal time series data and subtract that effect from the data:

  1. Identify an appropriate time series data set, this can be a data set in R or a data set you find.
  2. Then, use R to display decompose the seasonal time series and seasonally adjust to subtract the seasonal components from the time series.
  3. In your report, provide insights to the results.

B. Address issues of correlations between successive values of the time series:

  1. Identify an appropriate data set, this can be this can be a data set in R or a data set you find.
  2. Then use R time series functions to address autocorrelation issues in data sets. In many cases you can make a better predictive model by taking correlations in the data into account. Address autocorrelation issues (irregular components of the time series) using a technique called ARIMA (Autoregressive Integrated Moving Average) models for irregular components of time series.
  3. In your report, provide insights to the results.


Your assignment/project should have a good cover/title page, introduction of what the goals of the project and the methods you use. It also should follow APA format with at least 1000 words (excluding title page and references page) and references page. In the body of your project you should incorporate the R codes and R outputs with interpretation of your results. Finally, you need to make sense of your results to make good points with proper conclusions, to show your understanding of the course material and its application to the dataset.

Graphs, figures, charts, tables are very useful to increase visual effects to impress your readers. You also should do your best to give insight and understanding to the project with a good conclusion. Please use subtitles to make your assignment more reader friendly as well.

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