Extracting statistics from time series data

In order to extract meaningful insights from time series data, we can generate statistics from it. Examples of these statistics are operations like mean, variance, correlation, maximum value, and so on. These statistics can be computed on a rolling basis using a window. We can use a predetermined window size and compute these statistics within that window. When we visualize the statistics over time, we might see interesting patterns. Let's see an example of how to extract these statistics 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...