My last project of automating a data import pipeline for Chicago's crime data created the perfect environment for using past crime rates to predict future. I use SARIMA time-series forecasting to predict weekly crime rates 6-months out for the city and create a heatmap of location by time to further identify crime trends for Chicago.

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As in the last post, here's some more work in excel with economic variables. This time I use value forecasts of 30y mortgage, unemployment, and personal income rates, figured in a similar manner as before (annual growth/change rates - 10y moving averages) to predict future levels of personal consumption expenditures. I run a multilinear regression analysis to forecast PCE based upon the three independent variables and end up with some pretty strong results and an adjusted R-squared of .974.

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I grabbed some economic data from FRED and attempt to forecast future values for each variable using Excel. Econ variables of focus for this project include GDP, CPI, and the unemployment rate for the U.S. This was originally done for my Macro-econometrics class and provided me some nice time with Excel which I admittedly use too little.

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Lately I've been looking to explore time-series modeling and to get more practice with manipulating data in R. Luckily, a dataset on Kaggle provided me the opportunity to do both of these things. Attempting to predict future sale volume of items in stores (from 1-C russian store sales dataset) gave me the chance to apply the theoretical knowledge I've been studying of time-series analysis (ARIMA in particual). Additionally, I was able to get more comfortable with dplyr and lubridate in the process. more ...