| view 👀:118 |
🙍 oneddl |
redaktor: FreshWap.CC | Rating👍:
Udemy - Complete Practical Time Series Forecasting in Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 88 lectures (8h 34m) | Size: 2.72 GB
Learn Python, Time Series Model Additive, Multiplicative, AR, Moving Average, Exponential, ARIMA, SARIMAX, GARCH models
What you'll learn:
Python Programing
Basic to Advanced Time Series Methods
Time Series Visualization in Python
Auto Regressive Methods,
Moving Average, Exponential Moving Average
Linear Regression and Evaluation
Additive and Multiplicative Models
ARMA, ARIMA, SARIMA in Python
ACF and PACF
Auto ARIMA in Python
Stationary and Non Stationary
GARCH Models
Requirements
Basics knowledge in Statistics
Basic understand on Python
Should have Gmail Account and should able to open Google Drive
Description
Welcome to Complete Practical Time Series Analysis and Forecasting in Python
Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers all type of modeling techniques for forecasting and analysis.
We start with programming in Python which is the essential skill required and then we will exploring the fundamental time series theory to help you understand the modeling that comes afterward.
Then throughout the course, we will work with a number of Python libraries, providing you with complete training. We will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matDescriptionlib, statsmodels, Sklearn, and ARCH.
With these tools we will master the most widely used models out there:
- Additive Model
- Multiplicative Model
· AR (autoregressive model)
· Simple Moving Average
- Weighted Moving Average
- Exponential Moving Average
· ARMA (autoregressive-moving-average model)
· ARIMA (autoregressive integrated moving average model)
. SARIMA (seasonal autoregressive integrated moving average model)
. SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)
· ARCH (autoregressive conditional heteroscedasticity model)
· GARCH (generalized autoregressive conditional heteroscedasticity model)
We know that time series is one of those topics that always leaves some doubts.
Until now.
This course is exactly what you need to comprehend the time series once and for all. Not only that, but you will also get a ton of additional materials - notebooks files, course notes - everything is included.
Who this course is for
Anyone who are interested to do time series analysis and forecasting
Want to do advanced real time forecasting
Homepage
https://www.udemy.com/course/complete-practical-time-series-forecasting-in-python/
Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
https://rapidgator.net/file/cec860210d93ef169052ccd1aa7bef59/ij8m4.Complete.Practical.Time.Series.Forecasting.in.Python.part1.rar.html
https://rapidgator.net/file/414a2efc23e6ca3ed99d9a00a72e4627/ij8m4.Complete.Practical.Time.Series.Forecasting.in.Python.part2.rar.html
https://rapidgator.net/file/7fad20127bd69a54c5b69d8a26b5dfda/ij8m4.Complete.Practical.Time.Series.Forecasting.in.Python.part3.rar.html
Links are Interchangeable - No Password - Single Extraction
⚠️ Dead Link ?
You may submit a re-upload request using the search feature.
All requests are reviewed in accordance with our Content Policy.
Comments (0)
Users of Guests are not allowed to comment this publication.