Yun-chi just graduated from Centennial Collegiate Vocational Institute in Guelph, ON, Canada and will be starting his studies in mathematics at the University of Toronto this fall. He is extremely interested in mathematics and plans to pursue a career in mathematical research. He took computer programming courses throughout his high school career and likes to use the power of programming languages to solve mathematics problems. Outside of mathematics and computers, he loves playing the piano and the saxophone, swimming, and playing catch with his dad. He also watches baseball and American football and is extremely interested in the ever-growing role of statistics in these sports.
Computational Essay: Integer Partitions: A Brief Overview And A Combinatorics Application
Project: Time Series Summarization
Suppose one wants to know the average temperature in a time series.The only option right now is to calculate the average of every single data point for the entire time series, which is extremely inefficient. We want to examine how to divide time series into years, months, weeks, dates and hours so that we can simply extract values for the entire interval from databases to calculate information.
Summary of Results
We start by extracting the complete years that occur during this time series. By doing so, we created objects that will represent all the data points for that year. We may then continue this process and extract leftover months, six-day weeks (six-day weeks are more efficient than seven-day weeks), days and hours to find the most efficient combination of the time series and calculate relevant information, such as mean, maximum or minimum value during that time series.
I can examine whether inserting other time intervals (quarters, decades, centuries, minutes, seconds) will allow for an even more efficient processing and implement these intervals if it is indeed more efficient to do so. I can also use the time series summation to look at other data (Dow Jones Index, etc.) to generate relevant statistical analysis such as forecasting.