Forecasting involves making predictions. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments) or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an essential aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

• No prior knowledge of R or data science is required.

• Emphasis on applications of time-series analysis and forecasting rather than theory and mathematical derivations.

• Plenty of rigorous examples and quizzes for an extensive learning experience.

• All course contents are self-explanatory.

• All R codes and data sets and provided for replication and practice.

At the completion of this course, you will be able to

• Explore and visualize time series data.

• Apply and interpret time series regression results.

• Understand various methods to forecast time series data.

• Use general forecasting tools and models for different forecasting situations.

• Utilize statistical programs to compute, visualize, and analyze time-series data in economics, business, and the social sciences.

You will learn

• Exploring and visualizing time series in R.

• Benchmark methods of time series forecasting.

• Time series forecasting forecast accuracy.

• Linear regression models.

• Exponential smoothing.

• Stationarity, ADF, KPSS, differencing, etc.

• ARIMA, SARIMA, and ARIMAX (dynamic regression) models.

• Other forecasting models.