Created by Diego Fernandez | Video: h264, 1280×720 | Audio: AAC 48KHz 2ch | Duration: 06:59 H/M | Lec: 46 | 4.75 GB | Language: English | Sub: English [Auto-generated]Learn main advanced forecasting models concepts from proficient to expert level through a practical course with PythonWhat you’ll learnRead S&P 500® Index ETF prices data and perform advanced forecasting models operations by installing related packages and running code on Python PyCharm IDE.
Identify Box-Jenkins autoregressive integrated moving average model integration order through level and differentiated time series first order trend stationary augmented Dickey-Fuller unit root test.
Recognize autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions.
Estimate autoregressive integrated moving average models such as random walk with drift and differentiated first order autoregressive.
Identify seasonal autoregressive integrated moving average model seasonal integration order through level and seasonally differentiated time series first order seasonal stationary deterministic test.
Estimate seasonal autoregressive integrated moving average models such as seasonal random walk with drift and seasonally differentiated first order autoregressive.
Select non-seasonal or seasonal autoregressive integrated moving average model with lowest Akaike and Schwarz Bayesian information loss criteria.
Evaluate autoregressive integrated moving average models forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.
Identify general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average model squared residuals or forecasting errors second order stationary Engle autoregressive conditional heteroscedasticity test.
Recognize non-Gaussian general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average and general autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors multiple order stationary Jarque-Bera normality test.
Estimate autoregressive integrated moving average models with residuals or forecasting errors assumed as Gaussian or Student-t distributed and with Bollerslev simple, Nelson exponential or Glosten-Jagannathan-Runkle threshold general autoregressive conditional heteroscedasticity effects such as random walk with drift and differentiated first order autoregressive.
Assess autoregressive integrated moving average model with highest forecasting accuracy standardized residuals or forecasting errors strong white noise modelling requirement.
RequirementsPractical example data and Python code files provided with the course.
Prior basic Python programming language knowledge is useful but not required.
DescriptionLearn advanced forecasting models through a practical course with Python programming language using S&P 500® Index ETF prices historical data.
It explores main concepts from proficient to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your advanced investment management or sales forecasting research.
All of this while exploring the wisdom of best academics and practitioners in the field.
Become an Advanced Forecasting Models Expert in this Practical Course with PythonRead S&P 500® Index ETF prices data and perform advanced forecasting models operations by installing related packages and running code on Python PyCharm IDE.
Identify Box-Jenkins autoregressive integrated moving average model integration order through level and differentiated time series first order trend stationary augmented Dickey-Fuller unit root test.
Recognize autoregressive integrated moving average model autoregressive and moving average orders through autocorrelation and partial autocorrelation functions.
Estimate autoregressive integrated moving average models such as random walk with drift and differentiated first order autoregressive.
Identify seasonal autoregressive integrated moving average model seasonal integration order through level and seasonally differentiated time series first order seasonal stationary deterministic test.
Estimate seasonal autoregressive integrated moving average models such as seasonal random walk with drift and seasonally differentiated first order autoregressive.
Select non-seasonal or seasonal autoregressive integrated moving average model with lowest Akaike and Schwarz Bayesian information loss criteria.
Evaluate autoregressive integrated moving average models forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.
Identify general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average model squared residuals or forecasting errors second order stationary Engle autoregressive conditional heteroscedasticity test.
Recognize non-Gaussian general autoregressive conditional heteroscedasticity modelling need through autoregressive integrated moving average and general autoregressive conditional heteroscedasticity model with highest forecasting accuracy standardized residuals or forecasting errors multiple order stationary Jarque-Bera normality test.
Estimate autoregressive integrated moving average models with residuals or forecasting errors assumed as Gaussian or Student-t distributed and with Bollerslev simple, Nelson exponential or Glosten-Jagannathan-Runkle threshold general autoregressive conditional heteroscedasticity effects such as random walk with drift and differentiated first order autoregressive.
Assess autoregressive integrated moving average model with highest forecasting accuracy standardized residuals or forecasting errors strong white noise modelling requirement.
Become an Advanced Forecasting Models Expert and Put Your Knowledge in PracticeLearning advanced forecasting models is indispensable for finance careers in areas such as portfolio management and risk management.
It is also essential for academic careers in advanced applied statistics, econometrics and quantitative finance.
And it’s necessary for advanced sales forecasting research.
But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for advanced forecast modelling to achieve greater effectiveness.
Who this course is for?Undergraduates or postgraduates who want to learn about advanced forecasting models using Python programming language.
Academic researchers who wish to deepen their knowledge in advanced applied statistics, econometrics or quantitative finance.
Experienced finance professionals or business data scientists who desire to apply this knowledge in advanced investment management research or sales forecasting.
