{"product_id":"139745","title":"Prophet Time Series Data Analysis ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPBu8DAySXip4mih6wUCX6P6JYFr.png?v=1765076343\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Prophet Time Series Data Analysis \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/160153205\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cdiv\u003e We can't predict the future, but we can implement predictive models.\u003cbr\u003e Prophet is a powerful open-source tool that enables Python and R developers to build scalable time series forecasts. \u003cbr\u003eProphet is designed to produce high-quality results without parameter tuning or optimization.\u003cbr\u003e And with just a little bit of learning, anyone can intuitively adjust the model to dramatically improve their analysis results.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e This book covers Prophet's inner workings step-by-step, from predictive models to implementing Prophet's cutting-edge predictive techniques.\u003cbr\u003e We provide fully working model examples based on raw data from a variety of topics, empowering you with the knowledge needed to model future data with greater accuracy, with less code.\u003cbr\u003e If you follow these steps carefully, you'll be able to utilize Prophet as well as the highly skilled engineers in Meta.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Prophet, an open-source time series forecasting tool from Meta (Facebook) that captures rich context with short codes without requiring specialized knowledge.\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e PART 01 Getting Started with Prophet\u003cbr\u003e __01. \u003cbr\u003eHistory of time series forecasting\u003cbr\u003e ____01-1 Time Series Forecasting Overview\u003cbr\u003e __1.1.1 Dependent Data Problem\u003cbr\u003e ____01-2 Moving average and exponential smoothing\u003cbr\u003e ____01-3 Autoregressive Cumulative Moving Average (ARIMA)\u003cbr\u003e ____01-4 ARCH\/GARCH\u003cbr\u003e ____01-5 Neural Network\u003cbr\u003e ____01-6 Prophet\u003cbr\u003e ____01-7 Recent Developments\u003cbr\u003e __1.7.1 NeuralProphet\u003cbr\u003e __1.7.2 Google's \"Robust Large-Scale Time Series Forecasting\"\u003cbr\u003e __1.7.3 LinkedIn's SilverKite\/GreyKite\u003cbr\u003e __1.7.4 Uber's Orbit\u003cbr\u003e __02.\u003cbr\u003e Prophet Start\u003cbr\u003e ____02-1 Download the Colab notebook file\u003cbr\u003e ____02-2 Building a Simple Prophet Model\u003cbr\u003e ____02-3 Interpreting the Prediction Data Frame\u003cbr\u003e ____02-4 Understanding the Component Plot\u003cbr\u003e __03.\u003cbr\u003e How Prophet Works\u003cbr\u003e ____03-1 Why Facebook Built Prophet\u003cbr\u003e ____03-2 Analyst-in-the-loop Forecast\u003cbr\u003e ____03-3 Prophet Formula\u003cbr\u003e __3.4.1 Linear growth\u003cbr\u003e __3.4.2 Logistic Growth\u003cbr\u003e __3.4.3 Seasonality\u003cbr\u003e __3.4.4 Public holidays\u003cbr\u003e PART 02 Seasonality, Tuning, and Advanced Features\u003cbr\u003e __04.\u003cbr\u003e Data processing on a non-daily basis\u003cbr\u003e ____04-1 Monthly data usage\u003cbr\u003e ____04-2 Use data with a cycle shorter than one day \u003cbr\u003e____04-3 Using data with regular missing intervals\u003cbr\u003e __05.\u003cbr\u003e Seasonality handling\u003cbr\u003e ____05-1 Additive vs. Multiplicative Seasonality\u003cbr\u003e ____05-2 Seasonality control by Fourier order\u003cbr\u003e ____05-3 Added custom seasonality\u003cbr\u003e ____05-4 Added conditional seasonality\u003cbr\u003e ____05-5 Seasonal Regulation\u003cbr\u003e __5.5.1 Global seasonal regulation\u003cbr\u003e __5.5.2 Local seasonal regulation\u003cbr\u003e __06.\u003cbr\u003e Predicting the effect of public holidays\u003cbr\u003e ____06-1 Added basic national holidays\u003cbr\u003e ____06-2 Additional local government (state\/province) holidays\u003cbr\u003e ____06-3 Create a custom holiday\u003cbr\u003e ____06-4 Create holiday\u003cbr\u003e ____06-5 Public Holiday Regulations\u003cbr\u003e __6.5.1 Global Holiday Regulations\u003cbr\u003e __6.5.2 Individual Holiday Regulations\u003cbr\u003e __07.\u003cbr\u003e Growth mode adjustment\u003cbr\u003e ____07-1 Linear growth application\u003cbr\u003e ____07-2 Logistic function\u003cbr\u003e ____07-3 Applying logistic growth to predict saturation\u003cbr\u003e __7.3.1 Increasing Logistic Growth\u003cbr\u003e __7.3.2 Fluctuating upper limit cap\u003cbr\u003e __7.3.3 Decreasing Logistic Growth\u003cbr\u003e ____07-4 Flat growth application\u003cbr\u003e ____07-5 Create a custom trend\u003cbr\u003e __08.\u003cbr\u003e Adjusting trend change points\u003cbr\u003e ____08-1 Automatic detection of trend change points \u003cbr\u003e__8.1.1 Default Change Detection\u003cbr\u003e ____08-2 Changes Regulations\u003cbr\u003e ____08-3 Set custom change point location\u003cbr\u003e __09.\u003cbr\u003e Add explanatory variables\u003cbr\u003e ____09-1 Add binary value variable\u003cbr\u003e ____09-2 Add continuous variables\u003cbr\u003e Interpretation of coefficients ____09-3\u003cbr\u003e __10.\u003cbr\u003e Outliers and Special Events\u003cbr\u003e ____10-1 Correcting outliers that cause seasonal variation\u003cbr\u003e ____10-2 Correcting outliers that cause wide uncertainty intervals\u003cbr\u003e ____10-3 Automatic outlier detection\u003cbr\u003e __10.3.1 Windsor\u003cbr\u003e __10.3.2 Standard Deviation\u003cbr\u003e __10.3.3 Moving average\u003cbr\u003e __10.3.4 Standard deviation of error\u003cbr\u003e Modeling ____10-4 outliers as special events\u003cbr\u003e ____10-5 Modeling the Impact of COVID-19 Lockdowns\u003cbr\u003e __11.\u003cbr\u003e Uncertainty interval handling\u003cbr\u003e ____11-1 Trend Uncertainty Modeling\u003cbr\u003e ____11-2 Seasonal Uncertainty Modeling\u003cbr\u003e PART 03 DIAGNOSIS AND ASSESSMENT\u003cbr\u003e __12.\u003cbr\u003e Run cross validation\u003cbr\u003e ____12-1 k-fold cross-validation\u003cbr\u003e ____12-2 Forward chained cross-validation\u003cbr\u003e ____12-3 Creating a Prophet Cross-Validation DataFrame\u003cbr\u003e ____12-4 Parallel Cross Validation\u003cbr\u003e __13.\u003cbr\u003e Performance Indicator Evaluation\u003cbr\u003e ____13-1 Understanding the Prophet Indicator\u003cbr\u003e __13.1.1 MSE\u003cbr\u003e __13.1.2 RMSE \u003cbr\u003e__13.1.3 MAE\u003cbr\u003e __13.1.4 MAPE\u003cbr\u003e __13.1.5 MdAPE\u003cbr\u003e __13.1.6 SMAPE\u003cbr\u003e __13.1.7 Coverage\u003cbr\u003e __13.1.8 Selecting the optimal indicator\u003cbr\u003e ____13-2 Creating a Prophet Performance Metrics Dataframe\u003cbr\u003e ____13-3 Irregular cutoff processing\u003cbr\u003e ____13-4 Hyperparameter Tuning with Grid Search\u003cbr\u003e __14.\u003cbr\u003e Prophet commercialization\u003cbr\u003e ____14-1 Save the model\u003cbr\u003e ____14-2 Fit Model Update\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e This book will guide you through installing and setting up Prophet on your computer, and building your initial model with just a few lines of code.\u003cbr\u003e Next, we'll explore more advanced features, such as visualizing forecast results, adding holidays, seasonality, trend inflection points, and handling outliers.\u003cbr\u003e You will also learn why and how to modify each basic parameter.\u003cbr\u003e You will also learn to understand Fourier series and how they model seasonality, and how to apply them. \u003cbr\u003eYou will further learn how to optimize more complex models by tuning hyperparameters and including additional regressors in your model.\u003cbr\u003e You'll run diagnostics to evaluate model performance and learn useful features for deploying Prophet in real-world production environments.\u003cbr\u003e This will enable you to develop sophisticated and accurate forecasting models using concise, understandable, and reproducible code, leveraging raw time series datasets.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★Main topics covered in this book★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 1: The Evolution of Time Series Forecasting: From Early Attempts to Understand Time Series Data to the Present, Major Algorithm Developments\u003cbr\u003e Chapter 2: Getting Started with Prophet: The Basic Process of How Prophet Works and How to Build Your First Model\u003cbr\u003e Chapter 3: How Prophet Works: Why Meta Developed Prophet and the Analyst-in-the-Loop, Predictive Algorithm Formula \u003cbr\u003eChapter 4: Processing Non-Daily Data: How to Process and Use Various Periodic Data Other Than Daily Data\u003cbr\u003e Chapter 5: Seasonality Handling: The Concept and Control of Seasonality, a Key Element of the Prophet Model\u003cbr\u003e Chapter 6: Predicting the Effect of Holidays: Setting Default, Regional, and Custom Holidays and Reflecting the Effect of Holidays\u003cbr\u003e Chapter 7: Adjusting Growth Modes: Linear, Logistic, and Flat Trend Modes and Their Applications\u003cbr\u003e Chapter 8: Controlling Trend Changes: Adjusting Trend Line Rigidity and Flexibility to Build Optimal Models for Different Situations\u003cbr\u003e Chapter 9 Adding Explanatory Variables: How to Add Multivariate Input Data Using Explanatory Variables\u003cbr\u003e Chapter 10 Outliers and Special Events: Outlier Problems and Automatic Detection and Prophet-Based Processing Techniques\u003cbr\u003e Chapter 11 Handling Uncertainty Intervals: Quantifying Model Uncertainty and Visualizing Techniques\u003cbr\u003e Chapter 12: Implementing Cross-Validation: Cross-Validation Methods and Prophet for Time Series Data Characteristics \u003cbr\u003eChapter 13: Evaluating Performance Metrics: Optimizing Models and Improving Accuracy Using Performance Metrics and Cross-Validation\u003cbr\u003e Chapter 14: Prophet Productization: Model Saving and Update Methods and Dashboard Sharing Techniques Using Plotly\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★This book's target audience★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e * Business managers and data analysts who want to learn time series forecasting using Python or R.\u003cbr\u003e * Data scientists and analysts who want to understand the characteristics of time series data and the differences from other data types.\u003cbr\u003e * Machine learning engineers who want to apply machine learning and predictive modeling to real-world projects.\u003cbr\u003e * Software engineers who want to use predictive techniques while handling time series data in the software development process. \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e October 15, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 324 pages | 170*232*30mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791193083307\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1193083303 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default 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