Say I have two hypothetical time series data one for rainfall and the other is ocean surface temperature. Rainfall Time Series: 2001-12-31 25 mm 2002-12-31 50 mm 2003-12-31 75 mm 2004-12-31 50 mm 2005-12-31 25 mm 2006-12-31 10 mm 2007-12-31 6 mm 2008-12-31 8 mm 2009-12-31 10 mm 2010-12-31 12 mm 2011-12-31 20 mm 2012-12-31 75 mm Rainfall Time Series: 2001-12-31 36 (Degrees Celsius) 2002-12-31 37 (Degrees Celsius) 2003-12-31 38 (Degrees Celsius) 2004-12-31 37 (Degrees Celsius) 2005-12-31 36 (Degrees Celsius) 2006-12-31 34 (Degrees Celsius) 2007-12-31 32 (Degrees Celsius) 2008-12-31 33 (Degrees Celsius) 2009-12-31 34 (Degrees Celsius) 2010-12-31 35 (Degrees Celsius) 2011-12-31 35.9 (Degrees Celsius) 2012-12-31 38 (Degrees Celsius) I wanted to answer these questions: 1.) How are the two time series related? 2.) Is there a way to find out that if either one of time series changes the other one will also change? And if it does by how much? We know that rainfall and ocean surface temperature are related and not spurious (unlike the time series example of Airline Passengers and Rice Production in https://www.otexts.org/fpp/4/8) I have read about Approximate Entropy and Regression of two time series, I even read that ARIMA is also a good way to to determine the relationship of two time series data yet I havent found any good detailed example in python using scikit or pandas. I prefer to use Entropy but I do not know if it can answer question 2. I would also like to ask if there is a permutation distribution clustering in python, this seems to be an interesting way to solve question 1. Thanks for the help! Continue reading...
