{"id":26874,"date":"2024-02-28T08:01:53","date_gmt":"2024-02-28T09:01:53","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%da%a9%d8%aa%d8%a7%d8%a8-%d9%be%db%8c%d8%b4-%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d9%85%d8%af%d8%b1%d9%86-%d8%a8%d8%a7-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/"},"modified":"2024-08-21T16:27:42","modified_gmt":"2024-08-21T16:27:42","slug":"%da%a9%d8%aa%d8%a7%d8%a8-%d9%be%db%8c%d8%b4-%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d9%85%d8%af%d8%b1%d9%86-%d8%a8%d8%a7-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%da%a9%d8%aa%d8%a7%d8%a8-%d9%be%db%8c%d8%b4-%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d9%85%d8%af%d8%b1%d9%86-%d8%a8%d8%a7-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/","title":{"rendered":"\u06a9\u062a\u0627\u0628 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0645\u062f\u0631\u0646 \u0628\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646: \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0622\u0645\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u0635\u0646\u0639\u062a \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0645\u062f\u0631\u0646 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u06a9\u0627\u0648\u0634 \u06a9\u0646\u06cc\u062f"},"content":{"rendered":"<p>&nbsp;<\/p>\n<div style=\"text-align: center;\"><img decoding=\"async\" title=\"\u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u062a\u0627\u0628 Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning\" src=\"https:\/\/express24.ir\/d\/wp-content\/uploads\/2024\/02\/9781803246802.jpg\" alt=\"\u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u062a\u0627\u0628 Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning\" \/><\/div>\n<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\n<h3>\u0639\u0646\u0648\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc:<\/h3>\n<\/td>\n<td>\n<h3>Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning<\/h3>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0633\u0627\u0644 \u0627\u0646\u062a\u0634\u0627\u0631: 2022\u00a0\u00a0|\u00a0\u00a0552 \u0635\u0641\u062d\u0647\u00a0\u00a0|\u00a0\u00a0\u062d\u062c\u0645 \u0641\u0627\u06cc\u0644: 26 \u0645\u06af\u0627\u0628\u0627\u06cc\u062a\u00a0\u00a0|\u00a0\u00a0\u0632\u0628\u0627\u0646: \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u0647<\/td>\n<td>Manu Joseph<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0627\u0634\u0631<\/td>\n<td>Packt Publishing<\/td>\n<\/tr>\n<tr>\n<td>ISBN10:<\/td>\n<td>1803246804<\/td>\n<\/tr>\n<tr>\n<td>ISBN13:<\/td>\n<td>9781803246802<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06a9\u062a\u0627\u0628<\/h2>\n<div style=\"direction: ltr;\">Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts<br \/>\nKey FeaturesExplore industry-tested machine learning techniques used to forecast millions of time series<br \/>\nGet started with the revolutionary paradigm of global forecasting models<br \/>\nGet to grips with new concepts by applying them to real-world datasets of energy forecasting<\/p>\n<p>Book Description<\/p>\n<p>We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.<\/p>\n<p>This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You&#8217;ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you&#8217;ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.<\/p>\n<p>By the end of this book, you&#8217;ll be able to build world-class time series forecasting systems and tackle problems in the real world.<br \/>\nWhat you will learn<\/p>\n<p>Find out how to manipulate and visualize time series data like a pro<br \/>\nSet strong baselines with popular models such as ARIMA<br \/>\nDiscover how time series forecasting can be cast as regression<br \/>\nEngineer features for machine learning models for forecasting<br \/>\nExplore the exciting world of ensembling and stacking models<br \/>\nGet to grips with the global forecasting paradigm<br \/>\nUnderstand and apply state-of-the-art DL models such as N-BEATS and Autoformer<br \/>\nExplore multi-step forecasting and cross-validation strategies<\/p>\n<p>Who this book is for<\/p>\n<p>The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.<br \/>\nTable of Contents<\/p>\n<p>Introducing Time Series<br \/>\nAcquiring and Processing Time Series Data<br \/>\nAnalyzing and Visualizing Time Series Data<br \/>\nSetting a Strong Baseline Forecast<br \/>\nTime Series Forecasting as Regression<br \/>\nFeature Engineering for Time Series Forecasting<br \/>\nTarget Transformations for Time Series Forecasting<br \/>\nForecasting Time Series with Machine Learning Models<br \/>\nEnsembling and Stacking<br \/>\nGlobal Forecasting Models<br \/>\nIntroduction to Deep Learning<br \/>\nBuilding Blocks of Deep Learning for Time Series<br \/>\nCommon Modeling Patterns for Time Series<br \/>\nAttention and Transformers for Time Series<br \/>\nStrategies for Global Deep Learning Forecasting Models<\/p>\n<p>(N.B. Please use the Look Inside option to see further chapters)<\/p>\n<\/div>\n<h2>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u0633\u0627\u062e\u062a \u0633\u06cc\u0633\u062a\u0645 \u0647\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u062f\u0631 \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u06a9\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0645\u062f\u0631\u0646 \u0648 \u0645\u0641\u0627\u0647\u06cc\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0647 \u0645\u06cc\u0644\u06cc\u0648\u0646 \u0647\u0627 \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0645\u06cc \u062f\u0647\u0646\u062f<br \/>\n\u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u06a9\u0644\u06cc\u062f\u06cc<\/p>\n<p>\u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 \u0635\u0646\u0639\u062a \u0631\u0627 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc\u0644\u06cc\u0648\u0646 \u0647\u0627 \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u06a9\u0634\u0641 \u06a9\u0646\u06cc\u062f<br \/>\n\u0628\u0627 \u0627\u0644\u06af\u0648\u06cc \u0627\u0646\u0642\u0644\u0627\u0628\u06cc \u0645\u062f\u0644 \u0647\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062c\u0647\u0627\u0646\u06cc \u0634\u0631\u0648\u0639 \u06a9\u0646\u06cc\u062f<br \/>\n\u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646\u0647\u0627 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0627\u0646\u0631\u0698\u06cc \u060c \u0628\u0627 \u0645\u0641\u0627\u0647\u06cc\u0645 \u062c\u062f\u06cc\u062f \u0631\u0648\u0628\u0631\u0648 \u0634\u0648\u06cc\u062f<\/p>\n<p>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06a9\u062a\u0627\u0628<\/p>\n<p>\u0645\u0627 \u062f\u0631 \u06cc\u06a9 \u062f\u0648\u0631\u0647 \u0633\u0631\u0646\u0648\u0634\u062a \u0633\u0627\u0632 \u0632\u0646\u062f\u06af\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0627\u0646\u0641\u062c\u0627\u0631 \u062f\u0631 \u06a9\u0648\u0627\u0646\u062a\u0648\u0645\u06cc \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062c\u0645\u0639 \u0622\u0648\u0631\u06cc \u0634\u062f\u0647 \u0648 \u0639\u0644\u0627\u0642\u0647 \u062a\u0627\u0632\u0647 \u0627\u06cc \u0628\u0647 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u062f\u0627\u062f\u0647 \u0645\u062d\u0648\u0631 \u0645\u0627\u0646\u0646\u062f \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 (ML) \u060c \u0686\u0634\u0645 \u0627\u0646\u062f\u0627\u0632 \u062a\u062d\u0644\u06cc\u0644\u06cc \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0627\u062f\u0647 \u0648 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0627\u062f\u0647 \u0627\u0633\u062a.\u0627\u06cc\u0646 \u06a9\u062a\u0627\u0628 \u060c \u067e\u0631 \u0627\u0632 \u0646\u06a9\u0627\u062a \u0648 \u062a\u0631\u0641\u0646\u062f\u0647\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634 \u0634\u062f\u0647 \u0628\u0627 \u0635\u0646\u0639\u062a \u060c \u0634\u0645\u0627 \u0631\u0627 \u0641\u0631\u0627\u062a\u0631 \u0627\u0632 \u0631\u0648\u0634\u0647\u0627\u06cc \u0622\u0645\u0627\u0631\u06cc \u06a9\u0644\u0627\u0633\u06cc\u06a9 \u0645\u062a\u062f\u0627\u0648\u0644 \u0645\u0627\u0646\u0646\u062f Arima \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0622\u062e\u0631\u06cc\u0646 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u062f\u0646\u06cc\u0627\u06cc ML \u0631\u0627 \u0628\u0631\u0627\u06cc \u0634\u0645\u0627 \u0645\u0639\u0631\u0641\u06cc \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0627\u06cc\u0646 \u06cc\u06a9 \u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u062c\u0627\u0645\u0639 \u0628\u0631\u0627\u06cc \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u060c \u062a\u062c\u0633\u0645 \u0648 \u0627\u06cc\u062c\u0627\u062f \u0633\u06cc\u0633\u062a\u0645 \u0647\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u060c \u06a9\u0627\u0645\u0644 \u0628\u0627 \u0645\u0648\u0636\u0648\u0639\u0627\u062a \u0645\u0634\u062a\u0631\u06a9 \u0645\u0627\u0646\u0646\u062f ML \u0648 Deep Learning (DL) \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u0628\u0627\u062d\u062b \u0628\u0647 \u0646\u062f\u0631\u062a \u0644\u0645\u0633 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\u0632\u0645\u0627\u0646\u06cc \u0634\u0631\u0648\u0639 \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f.\u0627\u06cc\u0646 \u06a9\u062a\u0627\u0628 \u0634\u0645\u0627 \u0631\u0627 \u0628\u0647 \u06cc\u06a9 \u0633\u0641\u0631 \u062f\u0633\u062a\u06cc \u0645\u06cc \u0628\u0631\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0634\u0645\u0627 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u062a\u0631\u06cc\u0646 ML (\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0628\u0647 \u062f\u0631\u062e\u062a\u0627\u0646 \u0634\u06cc\u0628 \u062f\u0627\u0631) \u0648 DL (\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u062e\u0648\u0631\u0627\u06a9 \u0631\u0648 \u0628\u0647 \u062c\u0644\u0648 \u060c LSTM \u0647\u0627 \u0648 \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627) \u0631\u0627 \u062f\u0631 \u06cc\u06a9\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062f\u0646\u06cc\u0627\u06cc 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\u0628\u0631\u0627\u06cc \u062f\u06cc\u062f\u0646 \u0641\u0635\u0644 \u0647\u0627\u06cc \u0628\u0639\u062f\u06cc \u0627\u0632 \u06af\u0632\u06cc\u0646\u0647 Look Inside \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f)<br \/>\n<br \/>\r\n<table class=\"table table-bordered\">\r\n\t\r\n\t\t<tr>\r\n\t\t<td>\r\n\t\t\t\r\n\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0627\u06cc\u0646 \u0645\u062d\u0635\u0648\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc \u0627\u0633\u062a \u0648 \u0646\u0647 \u06a9\u062a\u0627\u0628 \u06a9\u0627\u063a\u0630\u06cc.\r\n\r\n\t\t<\/td>\r\n\t<\/tr>\r\n\r\n\t\t<tr>\r\n\t\t<td>\r\n\t\t\t\r\n\u0628\u0647 \u0647\u0646\u06af\u0627\u0645 \u062e\u0631\u06cc\u062f \u0628\u0647 \u0632\u0628\u0627\u0646 \u062f\u0631\u062c \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u06a9\u062a\u0627\u0628 \u062d\u062a\u0645\u0627 \u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f. \u0628\u0647 \u0635\u0648\u0631\u062a \u0645\u0639\u0645\u0648\u0644 \u062f\u0631 \u0627\u06a9\u062b\u0631 \u0645\u0648\u0627\u0631\u062f \u0632\u0628\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0641\u0627\u0631\u0633\u06cc \u0646\u06cc\u0633\u062a.\r\n\r\n\t\t<\/td>\r\n\t<\/tr>\t\r\n\r\n\t\t\t<tr>\r\n\t\t<td>\r\n\t\t\t\r\n\u062f\u0631 \u0635\u0648\u0631\u062a \u0647\u0631\u06af\u0648\u0646\u0647 \u0645\u0634\u06a9\u0644 \u062f\u0631 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0634\u0645\u0627\u0631\u0647 09395106248 \u067e\u06cc\u0627\u0645\u06a9 \u062f\u0647\u06cc\u062f. \r\n\t\t<\/td>\r\n\t<\/tr>\t\r\n\t\r\n\t\t\t<tr>\r\n\t\t<td>\r\n\t\t\t\r\n\u062f\u0631\u062c \u0634\u0645\u0627\u0631\u0647 \u0645\u0648\u0628\u0627\u06cc\u0644 \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u0636\u0631\u0648\u0631\u06cc \u0646\u06cc\u0633\u062a \u0648\u0644\u06cc \u062a\u0631\u062c\u06cc\u062d \u0622\u0646 \u0627\u0633\u062a \u062f\u0631\u062c \u06af\u0631\u062f\u062f \u062a\u0627 \u062f\u0631 \u0635\u0648\u0631\u062a \u0628\u0631\u0648\u0632 \u0645\u0634\u06a9\u0644 \u0627\u0648\u0644\u06cc\u0646 \u0631\u0627\u0647 \u0627\u0631\u062a\u0628\u0627\u0637\u06cc \u0645\u0627 \u0628\u0627 \u0634\u0645\u0627 \u0628\u0627\u0634\u062f.\r\n\t\t<\/td>\r\n\t<\/tr>\r\n\t\r\n\t<tr>\r\n\t\t<td>\r\n\t\t\t\r\n\t\t\t\u0686\u0646\u0627\u0646\u0686\u0647 \u062f\u0631 \u062f\u0631\u06cc\u0627\u0641\u062a \u0645\u062d\u0635\u0648\u0644 \u0628\u0647 \u0647\u0631 \u062f\u0644\u06cc\u0644\u06cc \u0628\u0627 \u0645\u0634\u06a9\u0644 \u0631\u0648\u0628\u0631\u0648 \u0634\u062f\u06cc\u062f \u0648 \u0645\u0637\u0645\u0626\u0646 \u0627\u0632 \u067e\u0631\u062f\u0627\u062e\u062a \u0645\u0648\u0641\u0642 \u0648\u062c\u0647 \u0647\u0633\u062a\u06cc\u062f \u0628\u0647 \u0634\u0645\u0627\u0631\u0647 \u062a\u0645\u0627\u0633 \u0632\u06cc\u0631 \u0646\u0627\u0645\u060c \u0646\u0627\u0645 \u062e\u0627\u0646\u0648\u0627\u062f\u06af\u06cc \u0648 \u0646\u0627\u0645 \u0645\u062d\u0635\u0648\u0644 \u0631\u0627 \u067e\u06cc\u0627\u0645\u06a9 \u0628\u0632\u0646\u06cc\u062f \u062a\u0627 \u0644\u06cc\u0646\u06a9 \u0645\u062d\u0635\u0648\u0644 \u0633\u0631\u06cc\u0639\u0627 \u0628\u0631\u0627\u06cc \u0634\u0645\u0627 \u0627\u0631\u0633\u0627\u0644 \u06af\u0631\u062f\u062f.\r\n\t\t\t<br \/><br \/>\r\n\t\t\t\u0634\u0645\u0627\u0631\u0647 \u062a\u0645\u0627\u0633: 09395106248 \r\n\r\n\t\t<\/td>\r\n\t<\/tr>\r\n\t\r\n<\/table>\r\n<br \/>\r\n\r\n<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; \u0639\u0646\u0648\u0627\u0646 \u06a9\u062a\u0627\u0628 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc: Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning [&hellip;]<\/p>\n","protected":false},"featured_media":26875,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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