I have made a weather prediction algorithm that predicts the maximum temprature in Memphis. I did this by following Brandon Rohrer’s Udemy course End-to-end data science: Time-series prediction. To predict the temprature I used the autocorrelation of one day’s temprature to the next to predict three days into the future. Autocorrelation is when the data at a previous timesteps is useful for predicting at a future timestep. The data had an Autocorrelation of .9, the data was from 1960 to 2018, and I got the data from www.ncdc.noaa.gov. I had to prepare the data by getting rid of missing values, seperating the data into maximum temprature and dates, and fixing some other minor bugs. In the end I achieved a Mean Absolute Error of 8.4. I’m planning to create a random forest model and turn it into an ios app using CoreML. For the date of June 29, 2018 the algorithm predicts a temprature of 90.01 degrees fahrenheit.

Scatter plot of the autocorrelation

The scatter plot shows that we have a high autocorrelation from the day before to the next.

A plot of the maximum temperatures

This plot show the maximum temperatures from 1960 to 2018

In the future, I plan to improve this model and integrate it into a app.