Companies, governments and other organizations now collect and analyze huge amounts of data about suppliers, clients, employees, citizens, transactions, and much more. There are a number of ways organizations can use this data. Business analytics uses this data to make better decisions and forecasting is an arm of this predictive analytics. Forecasting especially can provide a powerful toolkit for analyzing time series data.
Learn about forecasting in a wider context
Quantitative forecasting uses statistical and data mining methods to generating numerical forecasts, an important component of decision making across many business functions, including economic forecasting, workload projections, sales forecasts, and power and transportation demand. Today’s big data forecasting can include forecasting many series on a frequent basis, such as daily demand of thousands of products at retail chains, hourly statistics of wind turbines, minute-by-minute web traffic, and call volume to call centers.
Forecasting can also be combined with statistical monitoring methods for purposes of anomaly detection – for example, public health organizations collect and monitor clinical and other data for detecting disease outbreaks. Forecasting is also often combined with simulation for purposes of scenario building. On this course we’ll have a look at some of these uses in more depth as well as examining the processes that these different industries use.
Understand the forecasting process
This course focuses on forecasting time series, where past and present values are used to forecast future values of a series of interest. The course covers issues relating to different steps of the forecasting process, from goal definition, through data visualization, modeling, and performance evaluation to model deployment.
In this course you will:
Learn how to define a forecasting task and workflow
Understand how to evaluate forecasting performance
Apply and be familiar with popular forecasting methods
Explore, identify and model different types of patterns in time series
Be able to implement a forecasting process in practice
Familiarity with basic statistical methods including linear regression.