{"id":7460,"date":"2026-08-10T09:44:24","date_gmt":"2026-08-10T09:44:24","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-time-series-forecasting-in-python-video-edition-2022-11\/"},"modified":"2026-08-10T09:44:24","modified_gmt":"2026-08-10T09:44:24","slug":"oreilly-time-series-forecasting-in-python-video-edition-2022-11","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-time-series-forecasting-in-python-video-edition-2022-11\/","title":{"rendered":"Oreilly \u2013 Time Series Forecasting in Python, Video Edition 2022-11"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p data-sourcepos=\"7:1-7:303\"><span style=\"vertical-align: inherit\">Time Series Forecasting in Python Video Edition. This course is an audiobook-like experience. In this course, the narrator reads the book while the book content, charts, Python code, and explanatory text are displayed on the screen. The goal of this course is to teach you how to build forecasting models based on temporal patterns in your data. In this course, you will learn how to derive accurate and understandable forecasts from time-based data such as logs, customer analytics, and other event streams. By using statistical methods and deep learning for time series forecasting, you will be able to build powerful models.<\/span><\/p>\n<h3 data-sourcepos=\"9:1-9:25\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul data-sourcepos=\"11:1-16:0\">\n<li data-sourcepos=\"11:1-11:77\"><span style=\"vertical-align: inherit\">Identifying the time series forecasting problem and building high-performance forecasting models<\/span><\/li>\n<li data-sourcepos=\"12:1-12:83\"><span style=\"vertical-align: inherit\">Building univariate forecasting models that take into account seasonal effects and external variables<\/span><\/li>\n<li data-sourcepos=\"13:1-13:70\"><span style=\"vertical-align: inherit\">Building multivariate forecasting models to forecast multiple time series simultaneously<\/span><\/li>\n<li data-sourcepos=\"14:1-14:77\"><span style=\"vertical-align: inherit\">Using deep learning to predict time series in large datasets<\/span><\/li>\n<li data-sourcepos=\"15:1-16:0\"><span style=\"vertical-align: inherit\">Automate the forecasting process<\/span><\/li>\n<\/ul>\n<h3 data-sourcepos=\"17:1-17:33\"><span style=\"vertical-align: inherit\">This course is suitable for people who:<\/span><\/h3>\n<ul data-sourcepos=\"19:1-21:0\">\n<li data-sourcepos=\"19:1-19:64\"><span style=\"vertical-align: inherit\">Familiar with the Python programming language and the TensorFlow library<\/span><\/li>\n<li data-sourcepos=\"20:1-21:0\"><span style=\"vertical-align: inherit\">Work as a data scientist and seek to learn time series forecasting methods<\/span><\/li>\n<\/ul>\n<h3>Time Series Forecasting in Python Video Edition specification<\/h3>\n<ul>\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/time-series-forecasting\/9781617299889VE\/\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: &nbsp; <\/span><a href=\"https:\/\/downloadlynet.ir\/tag\/marco-peixeiro\/\"><span style=\"vertical-align: inherit\">Marco Peixeiro<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 11 hours and 4 minutes<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<ul>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Time waits for no one<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Understanding time series forecasting<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Bird\u2019s-eye view of time series forecasting<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. How time series forecasting is different from other regression tasks<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. A naive prediction of the future<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Implementing the historical mean baseline<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Forecasting last year\u2019s mean<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Going on a random walk<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Identifying a random walk<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Testing for stationarity<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. The autocorrelation function<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Forecasting a random walk<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Next steps<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. Forecasting with statistical models<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Modeling a moving average process<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Identifying the order of a moving average process<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Forecasting a moving average process Part 1<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Forecasting a moving average process Part 2<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Next steps<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Modeling an autoregressive process<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Finding the order of a stationary autoregressive process<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Forecasting an autoregressive process<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Modeling complex time series<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Examining the autoregressive moving average process<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Identifying a stationary ARMA process<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Devising a general modeling procedure<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Selecting a model using the AIC<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Understanding residual analysis<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Applying the general modeling procedure<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Forecasting bandwidth usage<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Forecasting non-stationary time series<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Forecasting a non-stationary time series Part 1<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Forecasting a non-stationary time series Part 2<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Accounting for seasonality<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Forecasting the number of monthly air passengers<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Forecasting with a SARIMA(p,d,q)(P,D,Q)m model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Adding external variables to our model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Exploring the exogenous variables of the US macroeconomics dataset<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Forecasting the real GDP using the SARIMAX model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Forecasting multiple time series<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Designing a modeling procedure for the VAR(p) model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Forecasting real disposable income and real consumption<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Next steps<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Performing model selection<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. Large-scale forecasting with deep learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Introducing deep learning for time series forecasting<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Getting ready to apply deep learning for forecasting<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Feature engineering and data splitting<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Data windowing and creating baselines for deep learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Implementing the DataWindow class<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Multi-step baseline models<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Baby steps with deep learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Implementing a multi-output linear model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Implementing a deep neural network as a multi-step model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Remembering the past with LSTM<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Examining the LSTM architecture<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Implementing the LSTM architecture<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Implementing an LSTM as a multi-output model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 16. Filtering a time series with CNN<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 16. Implementing a CNN<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 16. Implementing a CNN as a multi-step model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 17. Using predictions to make more predictions<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 17. Building an autoregressive LSTM model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 18. Capstone: Forecasting the electric power consumption of a household<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 18. Data wrangling and preprocessing<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 18. Feature engineering<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 18. Utility function to train our models<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 18. Long short-term memory (LSTM) model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 4. Automating forecasting at scale<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 19. Automating time series forecasting with Prophet<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 19. Exploring Prophet<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 19. Basic forecasting with Prophet<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 19. Exploring Prophet\u2019s advanced functionality<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 19. Hyperparameter tuning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 19. Forecasting project: Predicting the popularity of \u201cchocolate\u201d searches on Google<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 19. Experiment: Can SARIMA do better?<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 20. Capstone: Forecasting the monthly average retail price of steak in Canada<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 20. Modeling with Prophet<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 20. Optional: Develop a SARIMA model<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 21. Going above and beyond<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 21. Deep learning methods for forecasting<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 21. Other applications of time series data<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Time Series Forecasting in Python Video Edition course images<\/span><\/h3>\n<h2><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-949522 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2024\/12\/Time-Series-Forecasting-in-Python-Video-Edition.png\" alt=\"Time Series Forecasting in Python Video Edition\" width=\"1066\" height=\"368\"><\/h2>\n<h3><span style=\"vertical-align: inherit\">Sample course video<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-148358-1\"><video class=\"wp-video-shortcode\" id=\"video-148358-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Time_Series_Forecasting_in_Python_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Time_Series_Forecasting_in_Python_Video_Edition_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Time_Series_Forecasting_in_Python_Video_Edition_Downloadly.ir.mp4?nocache=1786109772491\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Time_Series_Forecasting_in_Python_Video_Edition_Downloadly.ir.mp4<\/a><\/video><\/mediaelementwrapper><\/div>\n<div class=\"mejs-layers\">\n<div class=\"mejs-poster mejs-layer\" style=\"display: none; width: 100%; height: 100%;\"><\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-loading\"><span class=\"mejs-overlay-loading-bg-img\"><\/span><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-error\"><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer mejs-overlay-play\" style=\"width: 100%; 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height: 100%;\"><\/div>\n<div class=\"mejs-volume-handle\" style=\"bottom: 100%; margin-bottom: -3px;\"><\/div>\n<\/div>\n<p><\/a><\/div>\n<div class=\"mejs-button mejs-fullscreen-button\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Fullscreen\" aria-label=\"Fullscreen\" tabindex=\"0\"><\/button><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div>\n<h3><span style=\"vertical-align: inherit\">Installation Guide<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">After Extract, view with your favorite player.<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Subtitles: None<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Quality: 720p<\/span><\/p>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">Download link<\/span><\/h3>\n<p><a href=\"https:\/\/dl2.downloadly.ir\/Files\/Elearning\/Oreilly_Time_Series_Forecasting_in_Python_Video_Edition_2022-11_Downloadly.ir.rar?nocache=1786109771\"><span style=\"vertical-align: inherit\">Download file \u2013 971 MB<\/span><\/a><\/p>\n<p><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><\/p>\n<h3><span style=\"vertical-align: inherit\">File size<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">971 MB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Time Series Forecasting in Python Video Edition. This course is an audiobook-like experience. 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