Business Analytics iCademy: Time Series in R Part 1

In this course we will introduce analytical methods to analyze time series data to build forecasting models and support decision-making. You will first learn the importance of moving averages for removing white noise and identifying trends. Building on concepts of moving average, you will be introduced to exponential smoothing techniques that place greater weight on more recent time series observations. While the moving average and simple exponential smoothing techniques are helpful for removing white noise, they often are not helpful for creating forecasts because they do not account for long-term trends and seasonal fluctuations in time series. For this reason, you will explore Holt’s exponential smoothing and Holt-Winters forecasting models to create more accurate forecasts. Finally, you will be introduced to autoregressive models, which are similar to “regular” linear regression models except that the explanatory variables are observations from prior periods, rather than other variables. Learning Outcomes - Examine moving averages and exponential smoothing in relation to time-series data - Identify three types of exponential smoothing methods: simple, Holt, and Holt-Winters - Execute the three types of exponential smoothing methods for creating forecasts of time-series data - Develop an understanding about autoregression, knowing its role in analyzing time- series data