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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...

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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Dimitri Shvorob Oct 21, 2017

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.

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Amazon Customer Oct 09, 2018

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.

Anil Kachroo Feb 19, 2020

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.

Amandeep Dec 10, 2019

I cant give zero rating...wrong code....missing data file....i m returning my book

Y Krinker Aug 01, 2019

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?

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

What do you get with Print?

Practical Time Series Analysis

Understanding Time Series Data

Advanced processing and visualization of time series data

Resampling time series data

Stationary processes

Time series decomposition

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

What do you get with Print?

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