Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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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...