Correlation between Time Series Indicators ( Stock Prices ) I am new to time series analysis and I am currently tackling a stock market prediction problem. 

I have a set of market indicators (such as Bollinger Bands, ADX etc) which are derived from the time dependent Open, High, Low and Close prices ( in Dollars ). 

I need to find correlations of this indicators with the Open, Low, High and Close prices over time and drop the features which are not correlated enough ( less than 0.70 )

I am working on python. Using Pandas I have tried the pandas.dataframe.corr() method as well, but I want to know if Pearson and Spearman correlation fucntions in pandas serve my purpose ? Is it the right way or is there another way of finding correct correlations ?

Thanks.
For time series, correlation is a different. A variable might b related past N values of other variables. 

This article explains theory behind finding relationships in time series (Skip to section "Stationarity in Time Series") : https://www.quantstart.com/articles/Serial-Correlation-in-Time-Series-Analysis

This is an implementation in Python : https://machinelearningmastery.com/gentle-introduction-autocorrelation-partial-autocorrelation/