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

Step by Step guide filled with real world practical examples.About This BookGet your first experience with data analysis with one of the most powerful types of analysistimeseries. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Timeseries methods using this examplerich 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 project Develop an understanding of loading, exploring, and visualizing timeseries data Explore autocorrelation and gain knowledge of statistical techniques to deal with nonstationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use autoregressive models to make predictions using timeseries data Build predictive models on time series using techniques based on autoregressive 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 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 reallife examples of time series and their analyses using cuttingedge 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 nonstationary 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 reallife problem scenarios and their solutions by bestpractice implementations in Python.The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.Style and approachThis book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.