TimeSerie analysis for stock volatility forecast and DTW clustering
This notebook intends to illustrate a few techniques and models to analyze TimeSeries and make forecast predictions.
This notebook illustrates how TimeSerie analysis and modelization can be used to evaluate and forecast volatility trends in Stocks, index, bonds or any financial products.
The goal of this notebook is to evaluate TimeSeries similitudes for a large number of stocks (either manually selected or from the S&P500) and cluster those Timeseries using Dynamic Time Warping into a given number (predefined) of clusters.
Note: The S&P500 (Standard and Poor's 500) stock market index is tracking the stock performance of 500 large companies listed on exchanges in the United States.