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Advanced Forecasting Models With Python

Course

ADVANCED FORECASTING MODELS WITH PYTHON

Category

Python Online Institute

Eligibility

Graduates and Technology Aspirants

Mode

Online and Offline Classes

Batches

Week Days and Week Ends

Duration :

1.5  hrs in weekdays and 3hrs during Weekend

Python What will you learn?

•How to Get Certified in Python.
•Python Script How to apply and use it.
•Learn how to build an app in Python.
•Cover all basic Concepts with in-depth description of Python.
•Learn Python from Scratch with Demos and Practical examples.
•How to build your own apps and scripts using Python.
•Understand and make use the new Features and Concepts in Python
•The students get hands on experience of how things happen in Python?
•Learn Python with hands-on coding exercises. Take your Python Skill to the next level

advanced forecasting models with python Course Features

•Free Aptitude classes & Mock interviews
•Course delivery through industry experts
•Accessibility of adequate training resources
•Trainer support after completion of the course
•Fast track and Sunday Batches available on request
•Project manager can be assigned to track candidates’ performance
•Training time :  Week Day / Week End – Any Day Any Time – Students can come and study
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises

Who are eligible for Python

•CNC Engineer, Software Developer, Testing Engineer, Implementation, Core Java, Struts, hibernate, Asp.net, c#, SQL Server, CNC Programming, backIOS Developer, .net c# asp.net, c c++ java, accounts finance sap fico, sap mm functional consultant
•Ms Crm, Guidewire, Sdm, Sde2, Qae, Sdet, Jbpm, Ext Js, Windows Admin, Full Stack, Aem, Spark, Hadoop, Big Data, Data Engineer, Azure, Cloud, OpentextQA Engineers, C++ Developers, Dot Net Developers, Mac Os Developers, Project Manager, Java Developers, Android Developers, IOS Developers
•Software Development, Big Data, Hadoop, Spark, Hive, Oozie, Big Data Analytics, Java, Python, R, Cloud, Data Quality, Scala, Nosql, Sql Database, Core Java

ADVANCED FORECASTING MODELS WITH PYTHON Topics

Course Overview
•Course •Advanced Forecasting Models
•Advanced Forecasting Models Data
•Course Data File
•Course Code Files
•Course Overview Slides
•Auto Regressive Integrated Moving Average Models
•ARIMA Models Slides
•ARIMA Models Overview
•ARIMA Models Specification
•Random Walk with Drift ARIMA Model
•Differentiated First Order ARIMA Model
•SARIMA Models Specification
•Seasonal Random Walk with Drift SARIMA Model
•Seasonally Differentiated First Order SARIMA Model
•ARIMA Model Selection
•ARIMA Models Forecasting Accuracy
•General Auto Regressive Conditional Heteroscedasticity Models
•GARCH Models Slides
•GARCH Models Overview
•GARCH Models Specification
•ARIMA-GARCH Models Estimation
•Random Walk with Drift GARCH Model
•Differentiated First Order Autoregressive GARCH Model
•ARIMA-EGARCH Models Estimation
•Random Walk with Drift EGARCH Model
•Differentiated First Order Autoregressive EGARCH Model
•ARIMA-GJR-GARCH Models Estimation
•Random Walk with Drift GJR-GARCH Model
•Differentiated First Order Autoregressive GJR-GARCH Model
•GARCH Model Selection
•GARCH Models Forecasting Accuracy
•Non-Gaussian General Auto Regressive Conditional Heteroscedasticity Models
•Non-Gaussian GARCH Models Slides
•Non-Gaussian GARCH Models Overview
•Non-Gaussian GARCH Models Specification
•ARIMA-GARCH-t Models Estimation
•Random Walk with Drift GARCH-t Model
•Differentiated First Order Autoregressive GARCH-t Model
•ARIMA-EGARCH-t Models Estimation
•Random Walk with Drift EGARCH-t Model
•Differentiated First Order Autoregressive EGARCH-t Model
•ARIMA-GJR-GARCH-t Models Estimation
•Random Walk with Drift GJR-GARCH-t Model
•Differentiated First Order Autoregressive GJR-GARCH-t Model
•Non-Gaussian GARCH Model Selection
•Non-Gaussian GARCH Models Forecasting Accuracy
•Residuals White Noise