Forecasting in R

Exponential smoothing is a forecast model used to generate accurate forecasts on univariate time series data. The model consists of a weighted sum of past observations, which exponentially decreases, giving more importance to the most recent observations. Sectors including finance, economics, and logistics will often use exponential smoothing.

To make accurate predictions using your time series data, it’s important that you plot your data in order to see patterns, unusual observations, and changes over time. You will also need to choose a suitable forecasting model, such as exponential smoothing methods or ARIMA models, and test your forecast accuracy. You may need to add more factors to your model to refine it, such as competitor activity or holidays.

Yes, this course is suitable for beginners. It provides a comprehensive introduction to time series forecasting using R, by exploring and visualizing time series in R, benchmarking methods and forecast accuracy, using exponential smoothing methods and forecasting with ARIMA models, as well as advanced methods.

Yes, upon completion of the course you will receive a certificate of completion.

This course can benefit many roles that demand predictive analysis and forecasting. This can include positions such as financial analysts, market researchers, and statisticians who need to understand and analyze data across multiple areas and create robust forecasting models.

Yes, this course covers advanced topics such as dealing with complicated seasonality and gathering external information. It also provides insights on how to extend ARIMA models and include the gathered information into the forecasting model.