第 1/1 張圖片
第 1/1 張圖片
Machine Learning for Time Series Forecasting with Python by Francesca Lazzeri (E
US $48.08
大約HK$ 374.21
狀況:
庫存 3 件
運費:
所在地:Fairfield, Ohio, 美國
送達日期:
估計於 8月21日, 三至 8月31日, 六之間送達 運送地點 84606
退貨:
保障:
請參閱物品說明或聯絡賣家以取得詳細資料。閱覽全部詳情查看保障詳情
(不符合「eBay 買家保障方案」資格)
安心購物
賣家必須承擔此刊登物品的所有責任。
eBay 物品編號:395492483322
物品細節
- 物品狀況
- 全新: 全新,未閱讀過和使用過的書籍,狀況完好,不存在缺頁或內頁受損。 查看所有物品狀況定義會在新視窗或分頁中開啟
- ISBN-13
- 9781119682363
- Book Title
- Machine Learning for Time Series Forecasting with Python
- ISBN
- 9781119682363
- Subject Area
- Mathematics, Computers, Science
- Publication Name
- Machine Learning for Time Series Forecasting with Python
- Publisher
- Wiley & Sons, Incorporated, John
- Item Length
- 9.1 in
- Subject
- General, Probability & Statistics / Time Series, Databases / Data Mining
- Publication Year
- 2020
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 0.5 in
- Item Weight
- 13.3 Oz
- Item Width
- 7.4 in
- Number of Pages
- 224 Pages
關於產品
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1119682363
ISBN-13
9781119682363
eBay Product ID (ePID)
24050077644
Product Key Features
Number of Pages
224 Pages
Language
English
Publication Name
Machine Learning for Time Series Forecasting with Python
Subject
General, Probability & Statistics / Time Series, Databases / Data Mining
Publication Year
2020
Type
Textbook
Subject Area
Mathematics, Computers, Science
Format
Trade Paperback
Dimensions
Item Height
0.5 in
Item Weight
13.3 Oz
Item Length
9.1 in
Item Width
7.4 in
Additional Product Features
Intended Audience
Scholarly & Professional
Dewey Edition
23
Dewey Decimal
006.31
Table Of Content
Acknowledgments vii Introduction xv Chapter 1 Overview of Time Series Forecasting 1 Flavors of Machine Learning for Time Series Forecasting 3 Supervised Learning for Time Series Forecasting 14 Python for Time Series Forecasting 21 Experimental Setup for Time Series Forecasting 24 Conclusion 26 Chapter 2 How to Design an End-to-End Time Series Forecasting Solution on the Cloud 29 Time Series Forecasting Template 31 Business Understanding and Performance Metrics 33 Data Ingestion 36 Data Exploration and Understanding 39 Data Pre-processing and Feature Engineering 40 Modeling Building and Selection 42 An Overview of Demand Forecasting Modeling Techniques 44 Model Evaluation 46 Model Deployment 48 Forecasting Solution Acceptance 53 Use Case: Demand Forecasting 54 Conclusion 58 Chapter 3 Time Series Data Preparation 61 Python for Time Series Data 62 Common Data Preparation Operations for Time Series 65 Time stamps vs. Periods 66 Converting to Timestamps 69 Providing a Format Argument 70 Indexing 71 Time/Date Components 76 Frequency Conversion 78 Time Series Exploration and Understanding 79 How to Get Started with Time Series Data Analysis 79 Data Cleaning of Missing Values in the Time Series 84 Time Series Data Normalization and Standardization 86 Time Series Feature Engineering 89 Date Time Features 90 Lag Features and Window Features 92 Rolling Window Statistics 95 Expanding Window Statistics 97 Conclusion 98 Chapter 4 Introduction to Autoregressive and Automated Methods for Time Series Forecasting 101 Autoregression 102 Moving Average 119 Autoregressive Moving Average 120 Autoregressive Integrated Moving Average 122 Automated Machine Learning 129 Conclusion 136 Chapter 5 Introduction to Neural Networks for Time Series Forecasting 137 Reasons to Add Deep Learning to Your Time Series Toolkit 138 Deep Learning Neural Networks Are Capable of Automatically Learning and Extracting Features from Raw and Imperfect Data 140 Deep Learning Supports Multiple Inputs and Outputs 142 Recurrent Neural Networks Are Good at Extracting Patterns from Input Data 143 Recurrent Neural Networks for Time Series Forecasting 144 Recurrent Neural Networks 145 Long Short-Term Memory 147 Gated Recurrent Unit 148 How to Prepare Time Series Data for LSTMs and GRUs 150 How to Develop GRUs and LSTMs for Time Series Forecasting 154 Keras 155 TensorFlow 156 Univariate Models 156 Multivariate Models 160 Conclusion 164 Chapter 6 Model Deployment for Time Series Forecasting 167 Experimental Set Up and Introduction to Azure Machine Learning SDK for Python 168 Workspace 169 Experiment 169 Run 169 Model 170 Compute Target, RunConfiguration, and ScriptRun Config 171 Image and Webservice 172 Machine Learning Model Deployment 173 How to Select the Right Tools to Succeed with Model Deployment 175 Solution Architecture for Time Series Forecasting with Deployment Examples 177 Train and Deploy an ARIMA Model 179 Configure the Workspace 182 Create an Experiment 183 Create or Attach a Compute Cluster 184 Upload the Data to Azure 184 Create an Estimator 188 Submit the Job to the Remote Cluster 188 Register the Model 189 Deployment 189 Define Your Entry Script and Dependencies 190 Automatic Schema Generation 191 Conclusion 196 References 197 Index 199
Synopsis
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the 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 distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and 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., One of the most important elements of today's decision-making world, in both the public and the private sectors, is the forecasting of macroeconomic and financial variables. This applies to many industries including finance, education, and health care to name just a few. However, not many business analysts or developers people know how to use machine learning approach and technologies to build successful forecast applications. This book provides a practical introductory guide to time series forecasting with machine learning and Python for those hands-on readers. Readers new to time series forecasting will be able to understand and deal better with: Time series forecasting concepts, such as horizon, frequency trend and seasonality. Evaluation of the time series forecasting models performance and accuracy. Understanding when to use neural networks instead of traditional time series models in time series forecasting. The book shows readers practical instances of how these time series forecasting models can be applied to a real-world scenario by providing examples and using many machine learning components available in open-source Python packages, such as Scikit-learn, Keras and Tensorflow. The reader will also use other Python tools such as Jupyter notebooks to interactively explore data, transform it, and then develop time series forecasting models.
LC Classification Number
Q325.5
賣家提供的物品說明
賣家必須承擔此刊登物品的所有責任。
eBay 物品編號:395492483322
運費與處理費
物品所在地:
Fairfield, Ohio, 美國
運送地點
不丹, 中國, 中國台灣, 中國澳門, 中國香港, 中非共和國, 丹麥, 乍得, 也門, 亞爾及利亞, 亞美尼亞, 以色列, 伊拉克, 伯利茲, 佛得角群島, 保加利亞, 克羅地亞共和國, 全球, 冰島, 列支敦士登, 利比里亞, 剛果共和國, 剛果民主共和國, 加拿大, 加納, 加蓬共和國, 匈牙利, 南非, 南韓, 博茨瓦納, 卡塔爾, 印尼, 印度, 危地馬拉, 厄瓜多爾, 厄立特里亞, 吉布提, 吉爾吉斯斯坦, 哈薩克斯坦, 哥倫比亞, 哥斯達黎加, 喀麥隆, 圖瓦盧, 土庫曼斯坦, 土耳其, 圭亞那, 坦桑尼亞, 埃及, 埃塞俄比亞, 基里巴斯, 塔吉克斯坦, 塞內加爾, 塞拉利昂, 塞浦路斯, 塞爾維亞, 塞舌爾, 墨西哥, 多哥, 多米尼克, 多米尼加共和國, 奧地利, 孟加拉, 安哥拉, 安圭拉島, 安提瓜和巴布達, 安道爾, 寮國, 尼加拉瓜, 尼日利亞, 尼日爾, 尼泊爾, 岡比亞, 巴哈馬, 巴基斯坦, 巴布亞新幾內亞, 巴拉圭, 巴拿馬, 巴林, 巴西, 布基納法索, 布隆迪, 希臘, 帕勞, 幾內亞, 幾內亞比紹, 庫克群島, 德國, 意大利, 愛沙尼亞, 愛爾蘭, 所羅門群島, 拉脫維亞, 挪威, 捷克共和國, 摩洛哥, 摩爾多瓦, 摩納哥, 文萊, 斐濟, 斯威士蘭, 斯洛伐克, 斯洛文尼亞, 斯瓦爾巴群島和揚馬延島, 斯里蘭卡, 新加坡, 新西蘭, 日本, 智利, 柬埔寨, 格恩西島, 格林納達, 格陵蘭, 格魯吉亞, 梵蒂岡, 比利時, 毛里塔尼亞, 毛里求斯, 沙特阿拉伯, 法國, 波多黎各, 波斯尼亞和黑塞哥維那, 波蘭, 泰國, 津巴布韋, 洪都拉斯, 海地, 湯加, 澤西島, 澳洲, 烏干達, 烏拉圭, 烏茲別克斯坦, 牙買加, 特克斯和凱科斯群島, 特立尼達和多巴哥, 玻利維亞, 瑙魯, 瑞典, 瑞士, 瓦利斯和富圖納群島, 瓦努阿圖, 百慕達群島, 盧旺達, 盧森堡, 直布羅陀, 福克蘭群島(馬爾維納斯), 科威特, 科摩羅, 科特迪瓦(象牙海岸), 秘魯, 突尼斯, 立陶宛, 米克羅尼西亞, 約旦, 納米比亞, 紐埃, 索馬里, 羅馬尼亞, 美屬維爾京群島, 美屬薩摩亞, 聖基茨-尼維斯, 聖文森特和格林納丁斯, 聖皮耶與密克隆群島, 聖盧西亞, 聖赫勒拿島, 聖馬力諾, 肯尼亞, 芬蘭, 英國, 英屬維爾京群島, 荷屬安地列斯群島, 荷蘭, 莫桑比克, 菲律賓, 萊索托, 葡萄牙, 蒙古, 蒙特塞拉特島, 薩爾瓦多, 蘇里南, 西撒哈拉, 西班牙, 西薩摩亞, 貝寧, 贊比亞, 赤道幾內亞, 越南, 開曼群島, 關島, 阿塞拜疆共和國, 阿富汗, 阿拉伯聯合酋長國, 阿曼, 阿根廷, 阿爾巴尼亞, 阿魯巴, 馬來西亞, 馬其頓, 馬拉維, 馬爾代夫, 馬約特島, 馬紹爾群島, 馬耳他, 馬達加斯加, 馬里, 黎巴嫩, 黑山
排除:
APO/FPO, 俄羅斯聯邦, 利比亞, 委內瑞拉, 巴巴多斯, 新喀里多尼亞, 法屬圭亞那, 法屬玻里尼西亞, 烏克蘭, 瓜德羅普島, 留尼汪島, 阿拉斯加/夏威夷, 馬提尼克島
運費與處理費 | 每加一件物品 | 運送地點 | 運送方式 | 運送*查看送達備註 |
---|---|---|---|---|
免運費 | 免費 | 美國 | Economy Shipping | 估計於 8月21日, 三至 8月31日, 六之間送達 運送地點 84606 |
處理時間 |
---|
通常會在收到所有款項後的 10 個工作日內發貨。 |
稅項 |
---|
結賬時相關稅項可能適用。 進一步了解進一步了解為 eBay 購物繳稅。 |
物品編號 395492483322 的銷售稅
物品編號 395492483322 的銷售稅
賣家會對寄往以下各州的物品收取銷售稅:
州/省 | 銷售稅稅率 |
---|
退貨政策
收到物品後聯絡賣家的期限: | 退款方式 |
---|---|
30 日 | 退款 |
買家負責支付退貨運費。
賣家信用評價 (1,028,841)
s***b (2994)- 買家留下的信用評價。
過去 1 個月
購買已獲認證
A fast and accurate transaction.
2***f (102)- 買家留下的信用評價。
過去 1 個月
購買已獲認證
excellent seller
k***b (276)- 買家留下的信用評價。
過去 1 個月
購買已獲認證
Happy with my purchase