{"product_id":"machine-learning-for-time-series-forecasting-with-python-9781119682363","title":"Machine Learning for Time Series Forecasting with","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn how to apply the principles of machine learning totime series modeling with thisindispensableresource Machine Learning for Time Series Forecasting with Pythonis an incisive and straightforward examination of one of the most crucial elements of decision-makingin finance,marketing,education, and healthcare:time series modeling. Despitethe centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguishedmachine learning scientistandeconomist,corrects that deficiency by providing readers withcomprehensiveand approachableexplanation andtreatment of the applicationof machine learning to time series forecasting. Written for readers who have little to no experience in time seriesforecastingor machine learning, the book comprehensively coversall the topics necessary to: Understand time series forecasting concepts, such asstationarity,horizon,trend,and seasonalityPrepare time series dataformodelingEvaluatetime series forecasting models'performance and accuracyUnderstand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Pythonis fullreal-world examples, resourcesand concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts,developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.  \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAcknowledgments vii\u003c\/p\u003e \u003cp\u003eIntroduction xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Overview of Time Series Forecasting 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFlavors of Machine Learning for Time Series Forecasting 3\u003c\/p\u003e \u003cp\u003eSupervised Learning for Time Series Forecasting 14\u003c\/p\u003e \u003cp\u003ePython for Time Series Forecasting 21\u003c\/p\u003e \u003cp\u003eExperimental Setup for Time Series Forecasting 24\u003c\/p\u003e \u003cp\u003eConclusion 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 How to Design an End-to-End Time Series Forecasting Solution on the Cloud 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTime Series Forecasting Template 31\u003c\/p\u003e \u003cp\u003eBusiness Understanding and Performance Metrics 33\u003c\/p\u003e \u003cp\u003eData Ingestion 36\u003c\/p\u003e \u003cp\u003eData Exploration and Understanding 39\u003c\/p\u003e \u003cp\u003eData Pre-processing and Feature Engineering 40\u003c\/p\u003e \u003cp\u003eModeling Building and Selection 42\u003c\/p\u003e \u003cp\u003eAn Overview of Demand Forecasting Modeling Techniques 44\u003c\/p\u003e \u003cp\u003eModel Evaluation 46\u003c\/p\u003e \u003cp\u003eModel Deployment 48\u003c\/p\u003e \u003cp\u003eForecasting Solution Acceptance 53\u003c\/p\u003e \u003cp\u003eUse Case: Demand Forecasting 54\u003c\/p\u003e \u003cp\u003eConclusion 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Time Series Data Preparation 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePython for Time Series Data 62\u003c\/p\u003e \u003cp\u003eCommon Data Preparation Operations for Time Series 65\u003c\/p\u003e \u003cp\u003eTime stamps vs. Periods 66\u003c\/p\u003e \u003cp\u003eConverting to Timestamps 69\u003c\/p\u003e \u003cp\u003eProviding a Format Argument 70\u003c\/p\u003e \u003cp\u003eIndexing 71\u003c\/p\u003e \u003cp\u003eTime\/Date Components 76\u003c\/p\u003e \u003cp\u003eFrequency Conversion 78\u003c\/p\u003e \u003cp\u003eTime Series Exploration and Understanding 79\u003c\/p\u003e \u003cp\u003eHow to Get Started with Time Series Data Analysis 79\u003c\/p\u003e \u003cp\u003eData Cleaning of Missing Values in the Time Series 84\u003c\/p\u003e \u003cp\u003eTime Series Data Normalization and Standardization 86\u003c\/p\u003e \u003cp\u003eTime Series Feature Engineering 89\u003c\/p\u003e \u003cp\u003eDate Time Features 90\u003c\/p\u003e \u003cp\u003eLag Features and Window Features 92\u003c\/p\u003e \u003cp\u003eRolling Window Statistics 95\u003c\/p\u003e \u003cp\u003eExpanding Window Statistics 97\u003c\/p\u003e \u003cp\u003eConclusion 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Introduction to Autoregressive and Automated Methods for Time Series Forecasting 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAutoregression 102\u003c\/p\u003e \u003cp\u003eMoving Average 119\u003c\/p\u003e \u003cp\u003eAutoregressive Moving Average 120\u003c\/p\u003e \u003cp\u003eAutoregressive Integrated Moving Average 122\u003c\/p\u003e \u003cp\u003eAutomated Machine Learning 129\u003c\/p\u003e \u003cp\u003eConclusion 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Introduction to Neural Networks for Time Series Forecasting 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReasons to Add Deep Learning to Your Time Series Toolkit 138\u003c\/p\u003e \u003cp\u003eDeep Learning Neural Networks Are Capable of Automatically Learning and Extracting Features from Raw and Imperfect Data 140\u003c\/p\u003e \u003cp\u003eDeep Learning Supports Multiple Inputs and Outputs 142\u003c\/p\u003e \u003cp\u003eRecurrent Neural Networks Are Good at Extracting Patterns from Input Data 143\u003c\/p\u003e \u003cp\u003eRecurrent Neural Networks for Time Series Forecasting 144\u003c\/p\u003e \u003cp\u003eRecurrent Neural Networks 145\u003c\/p\u003e \u003cp\u003eLong Short-Term Memory 147\u003c\/p\u003e \u003cp\u003eGated Recurrent Unit 148\u003c\/p\u003e \u003cp\u003eHow to Prepare Time Series Data for LSTMs and GRUs 150\u003c\/p\u003e \u003cp\u003eHow to Develop GRUs and LSTMs for Time Series Forecasting 154\u003c\/p\u003e \u003cp\u003eKeras 155\u003c\/p\u003e \u003cp\u003eTensorFlow 156\u003c\/p\u003e \u003cp\u003eUnivariate Models 156\u003c\/p\u003e \u003cp\u003eMultivariate Models 160\u003c\/p\u003e \u003cp\u003eConclusion 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Model Deployment for Time Series Forecasting 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExperimental Set Up and Introduction to Azure Machine Learning SDK for Python 168\u003c\/p\u003e \u003cp\u003eWorkspace 169\u003c\/p\u003e \u003cp\u003eExperiment 169\u003c\/p\u003e \u003cp\u003eRun 169\u003c\/p\u003e \u003cp\u003eModel 170\u003c\/p\u003e \u003cp\u003eCompute Target, RunConfiguration, and ScriptRun Config 171\u003c\/p\u003e \u003cp\u003eImage and Webservice 172\u003c\/p\u003e \u003cp\u003eMachine Learning Model Deployment 173\u003c\/p\u003e \u003cp\u003eHow to Select the Right Tools to Succeed with Model Deployment 175\u003c\/p\u003e \u003cp\u003eSolution Architecture for Time Series Forecasting with Deployment Examples 177\u003c\/p\u003e \u003cp\u003eTrain and Deploy an ARIMA Model 179\u003c\/p\u003e \u003cp\u003eConfigure the Workspace 182\u003c\/p\u003e \u003cp\u003eCreate an Experiment 183\u003c\/p\u003e \u003cp\u003eCreate or Attach a Compute Cluster 184\u003c\/p\u003e \u003cp\u003eUpload the Data to Azure 184\u003c\/p\u003e \u003cp\u003eCreate an Estimator 188\u003c\/p\u003e \u003cp\u003eSubmit the Job to the Remote Cluster 188\u003c\/p\u003e \u003cp\u003eRegister the Model 189\u003c\/p\u003e \u003cp\u003eDeployment 189\u003c\/p\u003e \u003cp\u003eDefine Your Entry Script and Dependencies 190\u003c\/p\u003e \u003cp\u003eAutomatic Schema Generation 191\u003c\/p\u003e \u003cp\u003eConclusion 196\u003c\/p\u003e \u003cp\u003eReferences 197\u003c\/p\u003e \u003cp\u003eIndex 199\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48738363801943,"sku":"9781119682363","price":35.62,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119682363.jpg?v=1723811979","url":"https:\/\/bookcurl.com\/products\/machine-learning-for-time-series-forecasting-with-python-9781119682363","provider":"Book Curl","version":"1.0","type":"link"}