Extracting statistics from time-series data

In order to extract meaningful insights from time-series data, we have to extract statistics from it. These stats can be things like mean, variance, correlation, maximum value, and so on. These stats have to be computed on a rolling basis using a window. We use a predetermined window size and keep computing these stats. When we visualize the stats over time, we will see interesting patterns. Let's see how to extract these stats from time-series data.

Create a new Python file and import the following packages:

import numpy as np 
import matplotlib.pyplot as plt 
import pandas as pd 
 
from timeseries import read_data  

Define the input filename:

# Input filename 
input_file = 'data_2D.txt' 

Load the third and fourth columns into separate variables:

# Load input data in time series format 
x1 = read_data(input_file, 2) 
x2 = read_data(input_file, 3) 

Create a pandas dataframe by naming the two dimensions:

# Create pandas dataframe for...