{"product_id":"110159","title":"Python Time Series Forecasting Analysis ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYEEOFjsxtLsO1qoN3tHmhJ26X2XT.png?v=1765095687\" 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 Python Time Series Forecasting 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\/128928864\/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\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLearning Time Series Analysis and Applications with Field Practical Examples\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e In data science, analyzing change patterns over time to build various predictive models can help predict the future and make various decisions.\u003cbr\u003e This book introduces a time series forecasting method that works perfectly with Python code.\u003cbr\u003e We'll cover defining time series data, developing baseline models, building large-scale models using statistical models, TensorFlow, and modern deep learning tools, and even an automated forecasting library.\u003cbr\u003e Master time series forecasting analysis with a variety of practical examples, including Google stock price trends, antidiabetic prescription predictions, and household electricity consumption forecasts.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e  \u003ch5\u003e\u003cb\u003eindex\u003c\/b\u003e\u003c\/h5\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e Translator's Preface xiii\u003cbr\u003e Beta Reader Review xiv\u003cbr\u003e Preface xvi\u003cbr\u003e Acknowledgments xviii\u003cbr\u003e About this book xix\u003cbr\u003e About the cover xxiii\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePART I Time waits for no one\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e CHAPTER 1 Understanding Time Series Forecasting 3\u003cbr\u003e 1.1 Introduction to Time Series 4\u003cbr\u003e __1.1.1 Components of a Time Series 5\u003cbr\u003e 1.2 Bird's-eye view of time series forecasting 8\u003cbr\u003e __1.2.1 Setting the Goal 10 \/ 1.2.2 Deciding What to Predict to Achieve the Goal 10 \/ 1.2.3 Setting the Forecast Period 10 \/ 1.2.4 Collecting Data 10 \/ 1.2.5 Developing the Forecast Model 11 \/ 1.2.6 Deploying to a Production Environment 12 \/ 1.2.7 Monitoring 12 \/ 1.2.8 Collecting New Data 12\u003cbr\u003e 1.3 How Time Series Forecasting Differs from Other Regression Tasks 13\u003cbr\u003e __1.3.1 Time series have an order 13 \/ 1.3.2 There are cases where time series have no features 14\u003cbr\u003e 1.4 Next Step 14\u003cbr\u003e Summary 15\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 2: Predicting the Future Simply 16\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Defining the Baseline Model 18\u003cbr\u003e 2.2 Predicting with Past Averages 19 \u003cbr\u003e__2.2.1 Setting up the baseline implementation 20 \/ 2.2.2 Implementing the historical average-based baseline model 22\u003cbr\u003e 2.3 Predicting with last year's average 27\u003cbr\u003e 2.4 Predicting with the last measured value 29\u003cbr\u003e 2.5 Implementing a Simple Seasonal Forecast 31\u003cbr\u003e 2.6 Next Step 32\u003cbr\u003e Summary 33\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 3: Following the Probabilistic Walk 35\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Stochastic Walk Process 37\u003cbr\u003e __3.1.1 Simulating a Stochastic Walk Process 37\u003cbr\u003e 3.2 Identifying Probabilistic Walks 40\u003cbr\u003e __3.2.1 Stationarity 42 \/ 3.2.2 Testing Stationarity 44 \/ 3.2.3 Autocorrelation Function 48 \/ 3.2.4 Putting It All Together 48 \/ 3.2.5 Is GOOGL a Random Walk? 52\u003cbr\u003e 3.3 Predicting a Stochastic Walk 55\u003cbr\u003e __3.3.1 Long-term Forecasting 55 \/ 3.3.2 Forecasting the Next Time Step 61\u003cbr\u003e 3.4 Next Step 64\u003cbr\u003e 3.5 Exercise 65\u003cbr\u003e __3.5.1 Simulating and Predicting a Stochastic Walk 65 \/ 3.5.2 Predicting the Daily Closing Price of GOOGL 66 \/ 3.5.3 Predicting the Daily Closing Price of a Directly Selected Stock 66\u003cbr\u003e Summary 67\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePART 2 Predicting Using Statistical Models\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e\u003cb\u003eCHAPTER 4 MODELING THE MOVING AVERAGE PROCESS 71\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Defining the Moving Average Process 73\u003cbr\u003e __4.1.1 Identifying the Order of the Moving Average Process 75\u003cbr\u003e 4.2 Predicting the Moving Average Process 80\u003cbr\u003e 4.3 Next Step 90\u003cbr\u003e 4.4 Exercise 91\u003cbr\u003e __4.4.1 MA(2) Process Simulation and Prediction 92 \/ 4.4.2 MA(q) Process Simulation and Prediction 92\u003cbr\u003e Summary 93\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 5: MODELING THE AUTOREVERSE PROCESS 94\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Predicting Average Weekly Footfall at a Retail Store 95\u003cbr\u003e 5.2 Defining the Autoregressive Process 97\u003cbr\u003e 5.3 Finding the Order of a Normal Autoregressive Process 98\u003cbr\u003e __5.3.1 Autocorrelation Function 104\u003cbr\u003e 5.4 Predicting Autoregressive Processes 107\u003cbr\u003e 5.5 Next Step 114\u003cbr\u003e 5.6 Exercise 114\u003cbr\u003e __5.6.1 Simulating AR(2) Processes and Making Predictions 114 \/ 5.6.2 Simulating AR(p) Processes and Making Predictions 115\u003cbr\u003e Summary 115\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 6: MODELING COMPLEX TIME SERIES 116\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Predicting Data Center Bandwidth Usage 117\u003cbr\u003e 6.2 Examining the Autoregressive Moving Average Process 120 \u003cbr\u003e6.3 Identifying Normal ARMA Processes 122\u003cbr\u003e 6.4 Designing a General Modeling Procedure 128\u003cbr\u003e __6.4.1 Understanding the Akaike Information Criterion 130 \/ 6.4.2 Selecting a Model Using AIC 132 \/ 6.4.3 Understanding Residual Analysis 134 \/ 6.4.4 Performing Residual Analysis 139\u003cbr\u003e 6.5 Applying General Modeling Procedures 143\u003cbr\u003e 6.6 Predicting Bandwidth Usage 152\u003cbr\u003e 6.7 Next Steps 157\u003cbr\u003e 6.8 Exercise 157\u003cbr\u003e __6.8.1 Performing Predictions on a Simulated ARMA(1,1) Process 158 \/ 6.8.2 Simulating an ARMA(2,2) Process and Performing Predictions 158\u003cbr\u003e Summary 159\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 7 Forecasting Nonstationary Time Series 161\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Defining the Autoregressive Cumulative Moving Average Model 164\u003cbr\u003e 7.2 Modifying General Modeling Procedures to Apply to Nonstationary Time Series 165\u003cbr\u003e 7.3 Forecasting Nonstationary Time Series 167\u003cbr\u003e 7.4 Next Steps 177\u003cbr\u003e 7.5 Exercise 177\u003cbr\u003e __7.5.1 Applying the ARIMA(p,d,q) Model to the Data Sets from Chapters 4, 5, and 6 177\u003cbr\u003e Summary 178\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 8 CONSIDERING SEASONALITY 179\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e8.1 Examining the SARIMA(p,d,q)(P,D,Q)m Model 180\u003cbr\u003e 8.2 Identifying Seasonal Patterns in Time Series 183\u003cbr\u003e 8.3 Forecasting Monthly Airline Passenger Counts 187\u003cbr\u003e __8.3.1 Forecasting using the ARIMA(p,d,q) model 190 \/ 8.3.2 Forecasting using the SARIMA(p,d,q)(P,D,Q)m model 196 \/ 8.3.3 Comparing the performance of each forecasting method 200\u003cbr\u003e 8.4 Next Steps 203\u003cbr\u003e 8.5 Exercise 203\u003cbr\u003e __8.5.1 Applying the SARIMA(p,d,q)(P,D,Q)m Model to the Johnson \u0026amp; Johnson Data Set 203\u003cbr\u003e Summary 204\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 9 Adding Exogenous Variables to the Model 205\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 Examining the SARIMAX Model 207\u003cbr\u003e __9.1.1 Exploring Exogenous Variables in the U.S. Macroeconomic Data Set 208 \/ 9.1.2 Considerations When Using SARIMAX 211\u003cbr\u003e 9.2 Forecasting Real GDP Using the SARIMAX Model 212\u003cbr\u003e 9.3 Next Steps 221\u003cbr\u003e 9.4 Exercise 222\u003cbr\u003e __9.4.1 Forecasting Real GDP Using All Exogenous Variables in a SARIMAX Model 222\u003cbr\u003e Summary 222\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 10 Forecasting Multiple Time Series 223\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 10.1 Examining the VAR Model 225\u003cbr\u003e 10.2 Designing a Modeling Procedure for a VAR(p) Model 227 \u003cbr\u003e__10.2.1 Examining the Granger Causality Test 229\u003cbr\u003e 10.3 Forecasting Real Disposable Income and Real Consumption 230\u003cbr\u003e 10.4 Next Steps 242\u003cbr\u003e 10.5 Exercise 243\u003cbr\u003e __10.5.1 Predicting realdpi and realcons using the VARMA model 243 \/10.5.2 Predicting realdpi and realcons using the VARMAX model 244\u003cbr\u003e Summary 244\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 11 Capstone Project: Predicting Antidiabetic Prescriptions in Australia 245\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 11.1 Importing required libraries and loading data 247\u003cbr\u003e 11.2 Visualizing Sequences and Their Components 248\u003cbr\u003e 11.3 Modeling with Data 250\u003cbr\u003e __11.3.1 Performing Model Selection 253 \/ 11.3.2 Performing Residual Analysis 254\u003cbr\u003e 11.4 Performing Predictions and Evaluating Model Performance 256\u003cbr\u003e 11.5 Next Step 260\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePART 3: Leveraging Deep Learning for Large-Scale Prediction\u003cbr\u003e\u003cbr\u003e CHAPTER 12: Introducing Deep Learning for Time Series Forecasting 263\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 12.1 When to Use Deep Learning for Time Series Forecasting 264\u003cbr\u003e 12.2 Examining Different Types of Deep Learning Models 265 \u003cbr\u003e12.3 Preparing to Apply Deep Learning for Prediction 268\u003cbr\u003e __12.3.1 Performing Data Exploration 268 \/ 12.3.2 Feature Engineering and Data Partitioning 272\u003cbr\u003e 12.4 Next Steps 277\u003cbr\u003e 12.5 Exercise 277\u003cbr\u003e Summary 278\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 13: Windowing Data and Building Baseline Models for Deep Learning 279\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 13.1 Creating a Data Window 280\u003cbr\u003e __13.1.1 A Look at Training a Deep Learning Model for Time Series Forecasting 280\u003cbr\u003e __13.1.2 Implementing the DataWindow Class 284\u003cbr\u003e 13.2 Applying the Baseline Model 292\u003cbr\u003e __13.2.1 Single-Step Baseline Model 292\u003cbr\u003e __13.2.2 Multi-stage baseline model 295\u003cbr\u003e __13.2.3 Multi-Output Baseline Model 299\u003cbr\u003e 13.3 Next Step 303\u003cbr\u003e 13.4 Exercise 303\u003cbr\u003e Summary 304\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 14: First Steps in Deep Learning 305\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 14.1 Implementing a Linear Model 306\u003cbr\u003e __14.1.1 Implementing a Single-Step Linear Model 307 \/ 14.1.2 Implementing a Multi-Step Linear Model 309 \/ 14.1.3 Implementing a Multi-Output Linear Model 311\u003cbr\u003e 14.2 Implementing a Deep Neural Network 312 \u003cbr\u003e__14.2.1 Implementing a Deep Neural Network as a Single-Stage Model 314 \/ 14.2.2 Implementing a Deep Neural Network as a Multi-Stage Model 317 \/ 14.2.3 Implementing a Deep Neural Network as a Multi-Output Model 319\u003cbr\u003e 14.3 Next Step 320\u003cbr\u003e 14.4 Exercise 321\u003cbr\u003e Summary 322\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 15: Remembering the Past with LSTMs 323\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 15.1 Examining Recurrent Neural Networks 324\u003cbr\u003e 15.2 Examining the LSTM Architecture 326\u003cbr\u003e __15.2.1 Forget Gate 327 \/ 15.2.2 Input Gate 329 \/ 15.2.3 Output Gate 330\u003cbr\u003e 15.3 Implementing the LSTM Architecture 332\u003cbr\u003e __15.3.1 Implementing LSTM as a Single-Stage Model 332 \/ 15.3.2 Implementing LSTM as a Multi-Stage Model 335 \/ 15.3.3 Implementing LSTM as a Multi-Output Model 338\u003cbr\u003e 15.4 Next Step 341\u003cbr\u003e 15.5 Exercise 342\u003cbr\u003e Summary 343\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 16 Filtering Time Series with CNNs 344\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 16.1 CNN Overview 345\u003cbr\u003e 16.2 Implementing CNNs 349\u003cbr\u003e __16.2.1 Implementing CNN as a Single-Stage Model 350 \/ 16.2.2 Implementing CNN as a Multi-Stage Model 354 \/ 16.2.3 Implementing CNN as a Multi-Output Model 356 \u003cbr\u003e16.3 Next Step 359\u003cbr\u003e 16.4 Exercise 359\u003cbr\u003e Summary 361\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 17: Predicting More with Predictions 362\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 17.1 A Look at the ARLSTM Architecture 363\u003cbr\u003e 17.2 Building an Autoregressive LSTM Model 364\u003cbr\u003e 17.3 Next Step 370\u003cbr\u003e 17.4 Exercise 371\u003cbr\u003e Summary 371\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 18 CAPSTONE PROJECT: ESTIMATE HOME POWER CONSUMPTION 372\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 18.1 Understanding the Capstone Project 373\u003cbr\u003e __18.1.1 Capstone Project Objectives 375\u003cbr\u003e 18.2 Data Wrangling and Preprocessing 376\u003cbr\u003e __18.2.1 Handling Missing Data 377 \/ 18.2.2 Data Transformation 379 \/ 18.2.3 Resampling Data 379\u003cbr\u003e 18.3 Feature Engineering 382\u003cbr\u003e __18.3.1 Removing Unnecessary Columns 383 \/ 18.3.2 Identifying Seasonal Periods 383 \/ 18.3.3 Partitioning and Scaling Data 386\u003cbr\u003e 18.4 Preparing to Model with Deep Learning 387\u003cbr\u003e __18.4.1 Initial Setup 387 \/ 18.4.2 Defining the DataWindow Class 389 \/ 18.4.3 Utility Functions for Model Training 391\u003cbr\u003e 18.5 Modeling with Deep Learning 392 \u003cbr\u003e__18.5.1 Baseline Model 392 \/ 18.5.2 Linear Model 396 \/ 18.5.3 Deep Neural Network 397 \/ 18.5.4 Long Short-Term Memory Model 398 \/ 18.5.5 Convolutional Neural Network 399 \/ 18.5.6 Combining CNN and LSTM 401 \/ 18.5.7 Autoregressive LSTM Model 402 \/ 18.5.8 Selecting the Optimal Model 404\u003cbr\u003e 18.6 Next Step 406\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePART 4 ​​Automating Large-Scale Forecasting\u003cbr\u003e\u003cbr\u003e CHAPTER 19 Automating Time Series Forecasting with Prophet 409\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 19.1 Overview of Automated Prediction Libraries 410\u003cbr\u003e 19.2 Examining the Prophet 412\u003cbr\u003e 19.3 Making Basic Predictions Using Prophet 414\u003cbr\u003e 19.4 Explore Prophet's Advanced Features 420\u003cbr\u003e __19.4.1 Visualization Features 421 \/ 19.4.2 Cross-Validation and Performance Metrics 425 \/ 19.4.3 Hyperparameter Tuning 429\u003cbr\u003e 19.5 Implementing a Robust Forecasting Procedure with Prophet 432\u003cbr\u003e __19.5.1 Prediction Project: Predicting the Popularity of 'Chocolate' Searches on Google 434 \/ 19.5.2 Experiment: Could SARIMA Be Better? 442\u003cbr\u003e 19.6 Next Step 446\u003cbr\u003e 19.7 Exercise 447 \u003cbr\u003e__19.7.1 Predicting the number of airline passengers 447 \/ 19.7.2 Predicting the number of antidiabetic prescriptions 447 \/ 19.7.3 Predicting keyword popularity in Google Trends 447\u003cbr\u003e Summary 448\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 20 CAPSTONE PROJECT: ESTIMATE THE AVERAGE MONTHLY RETAIL PRICE OF STEAK IN CANADA 449\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 20.1 Understanding the Capstone Project 450\u003cbr\u003e __20.1.1 Capstone Project Objective 450\u003cbr\u003e 20.2 Data Preprocessing and Visualization 451\u003cbr\u003e 20.3 Modeling with Prophet 453\u003cbr\u003e 20.4 Optional: Developing a SARIMA Model 459\u003cbr\u003e 20.5 Next Step 464\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 21 Going One Step Further 466\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 21.1 Summarizing What You've Learned 467\u003cbr\u003e __21.1.1 Statistical Methods for Prediction 467 \/ 21.1.2 Deep Learning Methods for Prediction 468 \/ 21.1.3 Automating the Prediction Process 469\u003cbr\u003e 21.2 What to do if your prediction fails? 470\u003cbr\u003e 21.3 Other Applications of Time Series Data 472\u003cbr\u003e 21.4 Continue practicing 473\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAPPENDIX A Installation Instructions 475\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Search 479\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4600\/MidCate001\/459909306.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eInto the book\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e  \u003cdiv\u003eThis book focuses entirely on time series forecasting.\u003cbr\u003e First, we will learn a simple prediction method that can serve as a baseline for more complex models.\u003cbr\u003e Next, we will perform predictions using two statistical learning techniques: the moving average model and the autoregressive model.\u003cbr\u003e These techniques will form the basis for more complex models that can handle non-stationarity, seasonal effects, and the influence of exogenous variables.\u003cbr\u003e We then move from statistical learning techniques to deep learning techniques, examining a scenario where deep learning outperforms statistical learning in predicting high-dimensional and large-scale time series.\u003cbr\u003e\u003cbr\u003e --- p.4\u003cbr\u003e\u003cbr\u003e Let's see if we can model GOOGL's daily closing price using a stochastic walk model.\u003cbr\u003e To do this, we must first check whether the process is normal. \u003cbr\u003eIf the process is abnormal, a transformation such as difference must be applied to make the process normal.\u003cbr\u003e Then, we can plot the autocorrelation function (ACF) to see if the daily closing price of GOOGL can be approximated by a stochastic walk model.\u003cbr\u003e In this chapter, we will cover both differential and autocorrelation function plots.\u003cbr\u003e Finally, we will conclude this chapter by predicting the future closing price of GOOGL.\u003cbr\u003e\u003cbr\u003e --- p.36\u003cbr\u003e\u003cbr\u003e In this chapter, we will examine ARMA(p,q), an autoregressive moving average process.\u003cbr\u003e Here, p represents the degree of the autoregressive part, and q represents the degree of the moving average part.\u003cbr\u003e (Omitted) This procedure includes model selection using the Akaike information criterion (AIC), which determines the optimal combination of p and q for the time series. \u003cbr\u003eThen, the validity of the model should be evaluated through residual analysis, which examines the QQ plot and density plot, which are correlation plots of the model residuals, to assess whether the model residuals are similar to white noise.\u003cbr\u003e If it is determined to be valid, we can move on to predicting the time series using the ARMA(p,q) model.\u003cbr\u003e\u003cbr\u003e --- p.117\u003cbr\u003e\u003cbr\u003e For example, let's say we need to predict hourly temperatures.\u003cbr\u003e It is reasonable to assume that there is daily seasonality, as temperatures tend to be lower at night and higher during the day, but there is also annual seasonality, as temperatures are lower in winter and higher in summer.\u003cbr\u003e In these cases, deep learning can be used to make predictions by leveraging information from both seasons.\u003cbr\u003e (Omitted) Ultimately, deep learning is used when fitting a statistical model takes too much time or when there are residuals that are correlated and not close to white noise. \u003cbr\u003eThis may be because there are other seasonal periods that the model cannot account for, or simply because there is a nonlinear relationship between the features and the target.\u003cbr\u003e In these cases, deep learning models can be used to capture these nonlinear relationships, with the added benefit of being very fast to train.\u003cbr\u003e\u003cbr\u003e --- p.264\u003cbr\u003e\u003cbr\u003e Let's look at an advanced architecture called the long short-term memory (LSTM), which is a specific example of a recurrent neural network (RNN).\u003cbr\u003e This type of neural network is used to process data arrays where order is important. One common application of RNNs and LSTMs is natural language processing.\u003cbr\u003e The words in a sentence have a certain order, and changing that order can completely change the meaning of the sentence.\u003cbr\u003e Therefore, this architecture is often behind text classification or text generation algorithms.\u003cbr\u003e \/ Another situation where the order of data is important is time series. \u003cbr\u003eWe know that a time series is a sequence of data occupying equal intervals in time and that the order cannot be changed.\u003cbr\u003e A data point observed at 9:00 AM must be before a data point observed at 10:00 AM and after a data point observed at 8:00 AM.\u003cbr\u003e Therefore, it is reasonable to apply the LSTM architecture to predict time series.\u003cbr\u003e\u003cbr\u003e --- p.323\u003cbr\u003e\u003cbr\u003e While manually building and tuning models provides flexibility and complete control over forecasting techniques, automating most of the forecasting process makes it easier to forecast time series and accelerates experimentation.\u003cbr\u003e Understanding automated tools is important because they allow us to get predictions quickly and often facilitate the use of state-of-the-art models.\u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e --- p.409\u003c\/div\u003e\n\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 \u003cb\u003eTime Series Data Science: Transitioning from R to Python\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eWhile R is a great language for traditional statistical analysis, replacing it with the near-universal Python opens up a wide range of applications, from statistical analysis to deep learning models and automated prediction libraries.\u003cbr\u003e Marcou Peycheiro studied time series forecasting and converted a lot of code written in R to Python, and wrote this book to provide a comprehensive reference for Python-based time series forecasting.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e This book covers everything from predictive analytics based on statistical models such as moving averages, autoregression, and SARIMAX using Python, to deep learning-based predictions such as LSTM and CNN architectures, and automated prediction libraries using Prophet and SARIMAX models.\u003cbr\u003e In particular, it shows the process of collecting data, building a model, and finding predicted values ​​step by step through appropriate examples for each topic. \u003cbr\u003eAs readers follow the exercises, they will experience the predicted and actual values ​​gradually getting closer.\u003cbr\u003e\u003cbr\u003e In data science, time variability is an important factor that cannot be ignored.\u003cbr\u003e Let's learn time series forecasting analysis techniques step by step through various practical examples, such as Google stock price trends, data center bandwidth usage forecasting, monthly airline passenger count forecasting, antidiabetic prescriptions forecasting, and household electricity consumption forecasting.\u003cbr\u003e After completing this book, you will learn a variety of time series data science techniques that can be applied immediately in your work.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eKey Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ● Concept of time series data and development of basic models\u003cbr\u003e ● Forecasting based on statistical models such as moving average, autoregression, and SARIMAX\u003cbr\u003e ● Deep learning-based predictions, including LSTM and CNN architectures\u003cbr\u003e ● Prophet, an automated prediction library using the SARIMAX model \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 July 25, 2024\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 508 pages | 1,004g | 188*245*25mm\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 9791193926314 \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 Title","offer_id":43893683224618,"sku":"110159","price":50.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/dbb2900b66f24bf63521801bfc3f004d.jpg?v=1765411586","url":"https:\/\/librairie.coreenne.fr\/en\/products\/110159","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}