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Practical Time Series Analysis
Practical Time Series Analysis

Practical Time Series Analysis: Master Time Series Data Processing, Visualization, and Modeling using Python

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Profile Icon Avishek Pal Profile Icon PKS Prakash
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€18.99 per month
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.4 (5 Ratings)
Paperback Sep 2017 244 pages 1st Edition
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€26.98 €29.99
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€36.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon Avishek Pal Profile Icon PKS Prakash
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€18.99 per month
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.4 (5 Ratings)
Paperback Sep 2017 244 pages 1st Edition
eBook
€26.98 €29.99
Paperback
€36.99
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Free Trial
Renews at €18.99p/m
eBook
€26.98 €29.99
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Table of content icon View table of contents Preview book icon Preview Book

Practical Time Series Analysis

Understanding Time Series Data

In the previous chapter, we touched upon a general approach of time series analysis which consists of two main steps:

  • Data visualization to check the presence of trend, seasonality, and cyclical patterns
  • Adjustment of trend and seasonality to generate stationary series

Generating stationary data is important for enhancing the time series forecasting model. Deduction of the trend, seasonal, and cyclical components would leave us with irregular fluctuations which cannot be modeled by using only the time index as an explanatory variable. Therefore, in order to further improve forecasting, the irregular fluctuations are assumed to be independent and identically distributed (iid) observations and modeled by a linear regression on variables other than the time index.

For example, house prices might exhibit both trend and seasonal (for example, quarterly...

Advanced processing and visualization of time series data

In many cases, the original time series needs to be transformed into aggregate statistics. For example, observations in the original time series might have been recorded at every second; however, in order to perform any meaningful analysis, data must be aggregated every minute. This would need resampling the observations over periods that are longer than the granular time indices in the original data. The aggregate statistics, such as mean, median, and variance, is calculated for each of the longer periods of time.

Another example of data pre-processing for time series, is computing aggregates over similar segments in the data. Consider the monthly sales of cars manufactured by company X where the data exhibits monthly seasonality, due to which sales during a month of a given year shows patters similar to the sales of the...

Resampling time series data

The technique of resmapling is illustrated using a time series on chemical concentration readings taken every two hours between 1st January 1975 and 17th January 1975. The dataset has been downloaded from http://datamarket.com and is also available in the datasets folder of this book's GitHub repo.

We start by importing the packages required for running this example:

from __future__ import print_function 
import os 
import pandas as pd 
import numpy as np 
%matplotlib inline 
from matplotlib import pyplot as plt 

Then we set the working directory as follows:

os.chdir('D:/Practical Time Series') 

This is followed by reading the data from the CSV file in a pandas.DataFrame and displaying shape and the first 10 rows of the DataFrame:

df = pd.read_csv('datasets/chemical-concentration-readings.csv') 
print('Shape of the dataset...

Stationary processes

Properties of data such as central tendency, dispersion, skewness, and kurtosis are called sample statistics. Mean and variance are two of the most commonly used sample statistics. In any analysis, data is collected by gathering information from a sample of the larger population. Mean, variance, and other properties are then estimated based on the sample data. Hence these are referred to as sample statistics.

An important assumption in statistical estimation theory is that, for sample statistics to be reliable, the population does not undergo any fundamental or systemic shifts over the individuals in the sample or over the time during which the data has been collected. This assumption ensures that sample statistics do not alter and will hold for entities that are outside the sample used for their estimation.

This assumption also applies to time series analysis...

Time series decomposition

The objective of time series decomposition is to model the long-term trend and seasonality and estimate the overall time series as a combination of them. Two popular models for time series decomposition are:

  • Additive model
  • Multiplicative model

The additive model formulates the original time series (xt) as the sum of the trend cycle (Ft) and seasonal (St) components as follows:

xt = Ft + St + Єt

The residuals Єt obtained after adjusting the trend and seasonal components are the irregular variations. The additive model is usually applied when there is a time-dependent trend cycle component, but independent seasonality that does not change over time.

The multiplicative decomposition model, which gives the time series as product of the trend, seasonal, and irregular components is useful when there is time-varying seasonality:

xt = Ft x St x...

Summary

We started this chapter by discussing advanced data processing techniques such as resampling, group-by, and moving window computations to obtain aggregate statistics from a time series. Next, we described stationary time series and discussed statistical tests of hypothesis such as Ljung-Box test and Augmented Dickey Fuller test to verify stationarity of a time series. Stationarizing non-stationary time series is important for time series forecasting. Therefore, we discussed two different approaches of stationarizing time series.
Firstly, the method of differencing, which covers first, second, and seasonal differencing, has been described for stationarizing a non-stationary time series. Secondly, time series decomposition using the statsmodels.tsa API for additive and multiplicative models has been discussed.
In the next chapter, we delve deeper in techniques of exponential...

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Key benefits

  • Get your first experience with data analysis with one of the most powerful types of analysis—time series.
  • Find patterns in your data and predict the future pattern based on historical data.
  • Learn the statistics, theory, and implementation of Time Series methods using this example-rich guide

Description

Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.

Who is this book for?

This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods.

What you will learn

  • • Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project
  • • Develop an understanding of loading, exploring, and visualizing time-series data
  • • Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series
  • • Take advantage of exponential smoothing to tackle noise in time series data
  • • Learn how to use auto-regressive models to make predictions using time series data
  • • Build predictive models on time series using techniques based on auto-regressive moving averages
  • • Discover recent advancements in deep learning to build accurate forecasting models for time series
  • • Gain familiarity with the basics of Python as a powerful yet simple to write programming language

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Publication date : Sep 28, 2017
Length: 244 pages
Edition : 1st
Language : English
ISBN-13 : 9781788290227
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Product Details

Publication date : Sep 28, 2017
Length: 244 pages
Edition : 1st
Language : English
ISBN-13 : 9781788290227
Category :
Languages :
Tools :

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Table of Contents

6 Chapters
Introduction to Time Series Chevron down icon Chevron up icon
Understanding Time Series Data Chevron down icon Chevron up icon
Exponential Smoothing based Methods Chevron down icon Chevron up icon
Auto-Regressive Models Chevron down icon Chevron up icon
Deep Learning for Time Series Forecasting Chevron down icon Chevron up icon
Getting Started with Python Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.4
(5 Ratings)
5 star 0%
4 star 0%
3 star 0%
2 star 40%
1 star 60%
Dimitri Shvorob Oct 21, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I had had higher expectations for a book co-authored by two Dr's. In the event, only portions of academic terminology and often-gratuitous formulas distinguish this book from a typical Packt "book product", written by an Indian IT worker. It is semi-competent and lazy, and riddled with typos; the most embarrassing ones are on page 119, where typos turn an AR model into an MA one. The authors' idea of time-series analysis is indeed the good old ARMA models, but bolted on to this material is a 20-plus-page section - these are short Packt pages, mind you - on neural networks, using Keras. Apart from the sketchy Keras code, you have tidbits of Python code using Pandas and Statmethods, and basic time-series plots done with Seaborn. If you are ready to pay $45 for the convenience of seeing this code, I envy you. My advice: stay away, and look elsewhere. I remember seeing a good R-based book, "Introductory Time Series with R" by Cowpertwait and Metcalf, but that was years ago, and better options must be available.
Amazon Verified review Amazon
Amazon Customer Oct 09, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This is a very elementary book about time series analysis using Python. It is suitable for undergraduate students without any prior knowledge on time series analysis and Python. But the book lacks many important topics such as ARCH/GARCH, vector autoregressions, vector error-correction model, etc., which can be taught in undergraduate classes. It is certainly not recommended for professional researchers.
Amazon Verified review Amazon
Anil Kachroo Feb 19, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I have realized that the tech books are not good for Kindle. Never Again.All tech book in paperback only. I will love if Amazon takes this book back and let me buy the same book in paperback.
Amazon Verified review Amazon
Amandeep Dec 10, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I cant give zero rating...wrong code....missing data file....i m returning my book
Amazon Verified review Amazon
Y Krinker Aug 01, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
It is hard to believe Packt would allow this book to be published... full of typos and bad code that won't even work most of the time.On top of that, there is zero analysis... more like, here is the plot of this time series... so what does this plot tell us? it is easy to plot it, but i would want you to tell me how to read this plot.total waste of time and to think that two Drs wrote this book! where did their get their degrees from?
Amazon Verified review Amazon
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