Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with...
This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains...
comp_dir = Path('data/store-sales-time-series-forecasting') book_sales = pd.read_csv( data_dir / 'book_sales.csv', index_col='Date', parse_dates=['Date'], ).drop('Paperback', axis=1) book_sales['Time'] = np.arange(len(book_sales.index)) book_sales['Lag_1'] = book_sales...
Time-Series Forecasting.(Book Reviews)(Book Review)Borkowf, Craig B
Like the title says, “Practical Time Series Forecasting with R” offers a hands-on guide and introduction to time series forecasting. The book explicitly focuses on the open-source program R and includes practical examples to teach various forecasting methods. Additionally, it covers popular forecas...
Springer Texts in Statistics(共117册), 这套丛书还有 《Stat Labs》《Design and Analysis of Experiments》《Plane Answers to Complex Questions: The Theory of Linear Models》《Statistical Analysis and Data Display》《》等。 我要写书评 Introduction to Time Series and Forecasting的书评 ···(全部 0 ...
You do not need to be a deep learning expert! You do not need to be a master of time series forecasting!…so what will YOU know after reading it? About Your Learning OutcomesThis book will teach you how to get results.After reading and working through this book, you will know:About...
An Introduction to Time Series Analysis and Forecasting豆瓣评分:0.0 简介:Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SA
comp_dir = Path('../input/store-sales-time-series-forecasting') retail_sales = pd.read_csv( data_dir / "us-retail-sales.csv", parse_dates=['Month'], index_col='Month', ).to_period('D') food_sales = retail_sales.loc[:, 'FoodAndBeverage'] ...