Predicting Google Search Trends
Final Project for CSCI 5466 - Chaotic Dynamics
The goals of this project were to see whether of not some Google search trends are predictable. Class presentation can be found in
To play with one of the trends already downloaded ("baseball", "influenza", and "full moon"), you may run:
python main.py "full moon"
This will generate a plot of the time series. It will also create plots of mutual information and percentage of false nearest neighbors - the steps required to delay-coordinate embed the time series. Finally, it will produce a prediction for the last 20% of the time series.
In blue is the real time series, and in red is our single-step forecast using Lorenz Method of Analogues with k=5.
I would highly recommend looking over Joshua Garland and Liz Bradley's paper "Prediction in Projection" for information about delay-coordinate embedding and prediction using Lorenz Method of Analogues (LMA).
J. Garland and E. Bradley, "Prediction in projection," Chaos 25:123108 (2015)
R. Hegger, H. Kantz, and T. Schreiber, Practical implementation of nonlinear time series methods: The TISEAN package, CHAOS 9, 413 (1999)