Practical Data Analysis Cookbook

Understanding time series data

One of the fundamental things when dealing with any dataset is to get intimate with it: without understanding what you are dealing with, you cannot build a successful statistical model.

Getting ready

To execute this recipe, you will need pandas, Statsmodels, and Matplotlib. No other prerequisites are required.

How to do it…

One of the fundamental statistics to check for any time series is the autocorrelation function (ACF), partial autocorrelation function (PACF), and spectral density (the ts_timeSeriesFunctions.py file):

import statsmodels as sm # read the data riverFlows = pd.read_csv(data_folder + 'combined_flow.csv', index_col=0, parse_dates=[0]) # autocorrelation function acf = {} # to store the results for col in ...