{"product_id":"time-series-analysis-with-python-cookbook-practical-recipes-for-exploratory-data-analysis-data-preparation-forecasting-and-model-evaluation-paperback","title":"Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eTarek A. Atwan\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePerform time series analysis and forecasting confidently with this Python code bank and reference manual\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms\u003c\/li\u003e\n\u003cli\u003eLearn different techniques for evaluating, diagnosing, and optimizing your models\u003c\/li\u003e\n\u003cli\u003eWork with a variety of complex data with trends, multiple seasonal patterns, and irregularities\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eTime series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.\u003c\/p\u003e\u003cp\u003eThis book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch.\u003c\/p\u003e\u003cp\u003eFinally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand what makes time series data different from other data\u003c\/li\u003e\n\u003cli\u003eApply various imputation and interpolation strategies for missing data\u003c\/li\u003e\n\u003cli\u003eImplement different models for univariate and multivariate time series\u003c\/li\u003e\n\u003cli\u003eUse different deep learning libraries such as TensorFlow, Keras, and PyTorch\u003c\/li\u003e\n\u003cli\u003ePlot interactive time series visualizations using hvPlot\u003c\/li\u003e\n\u003cli\u003eExplore state-space models and the unobserved components model (UCM)\u003c\/li\u003e\n\u003cli\u003eDetect anomalies using statistical and machine learning methods\u003c\/li\u003e\n\u003cli\u003eForecast complex time series with multiple seasonal patterns\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 630\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.27 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 30, 2022\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47212646203641,"sku":"9781801075541","price":100.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/dU8wWHY4bXl6dnA5cFFQbnJpeWRudz09.webp?v=1768094847","url":"https:\/\/bookscloud.io\/products\/time-series-analysis-with-python-cookbook-practical-recipes-for-exploratory-data-analysis-data-preparation-forecasting-and-model-evaluation-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}