Practical time series analysis: master time series data processing, visualization, and modeling using Python

« About This BookGet 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 guideWho This Book Is ForThis 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 LearnUnderstand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science projectDevelop an understanding of loading, exploring, and visualizing time-series dataExplore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time seriesTake advantage of exponential smoothing to tackle noise in time series dataLearn how to use auto-regressive models to make predictions using time-series dataBuild predictive models on time series using techniques based on auto-regressive moving averagesDiscover recent advancements in deep learning to build accurate forecasting models for time seriesGain familiarity with the basics of Python as a powerful yet simple to write programming languageIn DetailTime 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. »--

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Cover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Introduction to Time Series; Different types of data; Cross-sectional data; Time series data; Panel data; Internal structures of time series; General trend; Seasonality; Run sequence plot; Seasonal sub series plot; Multiple box plots; Cyclical changes; Unexpected variations; Models for time series analysis; Zero mean models; Random walk; Trend models; Seasonality models; Autocorrelation and Partial autocorrelation; Summary.

Chapter 2: Understanding Time Series DataAdvanced processing and visualization of time series data; Resampling time series data; Group wise aggregation; Moving statistics; Stationary processes; Differencing; First-order differencing; Second-order differencing; Seasonal differencing; Augmented Dickey-Fuller test; Time series decomposition; Moving averages; Moving averages and their smoothing effect; Seasonal adjustment using moving average; Weighted moving average; Time series decomposition using moving averages; Time series decomposition using statsmodels.tsa; Summary.

Chapter 3: Exponential Smoothing based MethodsIntro…