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Choose a time series analysis question to address that is of particular interest
Choose a time series analysis question to address that is of particular interest to you in business. Find your own business time-series data set (prefer financial data set). The data set should be up to date. Perform EDA analysis, model building, model selection, model diagnosis, model validation, and prediction.
Please submit your final presentation slides, final executive report (3 pages limit) with appendix(no page limit), R code(or Python), excel files and peer evaluation form.
Your presentation time is 10 mins and 2 mins for Q&A on April 30 in the classroom.
After the presentation, you will have until May 5th to submit the final work.
Possible grading criteria:
+nice problem
+Nice coding
+Nice EDA analysis
+Well written executive report.
+Nice presentation
+data preprocessing, time series decomposition
+tried regression, smoothing, seasonal arima, dynamic regression and garch methods
+tried stl, neural networks, intervention, machine learning models, etc
+ Performed model selections
+performed model validation
+performed forecasting
+used the idea of the weighted combined model
(+very low frequency or very high frequency data)
+good analysis and recommendation
+class discussion
-the executive report needs to be short and brief.
– not sufficient EDA
-models contain errors
– The prediction part is not consistent with your conclusion
– Report writing can be improved.
– The presentation can be improved.
– R coding can be improved
– Need to try all kinds of different models
-not good analysis of reasoning
please do decomposition using stl
porject topic i chose is predicting stock prices using up to data data ideally from yahoo finance or another reputable source.
at least 3 models should be used and the best one should be chosen and explained. I think good models to use would be ARIMA, Prohet, and maybe HOLTSwinters or Sarima.
plot all models
use accuracy() to produce metrics
when creating the powerpoint please be sure to make it visually appealing be adding the graphs metrics
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