Time Series and Forecasting with R QATSFR-QA

QATSFR-QA
Price on request

This three-day course is aimed at those who are familiar with data analysis and are interested in learning about how to analyse, model, and yield value from time series assets.This course will be of interest if you are interested in developing your own skills to move from analytics to Data Science, or if you are working with Data Scientists and want to learn more about what is possible when working with time series data.You will be introduced to key concepts and tools for use in time series analysis and forecasting including time series characteristics, time series components, time-based statistics, model development, exploratory analysis and visualisation, as well as techniques and strategies for model deployment.Throughout the course you will engage in activities and discussions with one of our Data Science technical specialists. Theoretical modules are complimented with comprehensive practical labs.

Interpret time series visualisations and understanding the business need for forecasts
Identify decomposition components: trend, seasonality, noise
Identify methods for handling shocks
Calculate a moving average
Identify how regression methods can be applied in simple forecasts
Use R to forecast with Arima methods
Apply the ARIMA model development and testing process
Tune and assess forecasting models
Use Facebook Prophet
Build and evaluate a model using prophet
Understand deep learning approaches for time series modelling
Interpret a deep learning time series model
Evaluate a deep learning time series model
Work on a practical time series modelling problem.

Time Series and Forecasting with R

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