Introduction to Neural Networks

Artificial neural networks (ANNs) describe a specific class of machine learning algorithms designed to acquire their own knowledge by extracting useful patterns from data. ANNs are function approximators, mapping inputs to outputs, and are composed of many interconnected computational units, called neurons. Each individual neuron possesses little intrinsic approximation capability; however, when many neurons function cohesively together, their combined effects show remarkable learning performance. A new set of tutorials introduce you to this popular analytic technique.

Imprecise Regression

Imprecise regression is a generalization of linear regression that gives us stronger tools for modeling uncertainty. In a typical linear regression setting, we consider the input data to be precise observations: single points. In imprecise regression, we generalize the notion of observations to intervals rather than points. This allows us to more accurately represent scenarios with measurement error or other sources of uncertainty that make our input data “fuzzy”. This tutorial is based on the imprecise regression work in Cattaneo and Wiencierz (2012) and will teach you the fundamentals of this technique.

Moving Averages

Smoothing methods are a family of forecasting approaches that average values over multiple periods in order to reduce the noise and uncover patterns in the data. Moving averages are one such smoothing method. In this new time series tutorial, you will learn the basics of performing smoothing averages.

Benchmark Methods & Forecast Accuracy

In this new time series tutorial, you will learn general tools that are useful for many different forecasting situations. It will describe some methods for benchmark forecasting, methods for checking whether a forecasting model has adequately utilized the available information, and methods for measuring forecast accuracy. These are important tools to have in your forecasting toolbox as each are leveraged repeatedly as you develop and explore a range of forecasting methods.

New Tutorial on Exploring and Visualizing Time Series

Time series forecasting is performed in nearly every organization that works with quantifiable data. Retail stores forecast sales. Energy companies forecast reserves, production, demand, and prices. Educational institutions forecast enrollment. Goverments forecast tax receipts and spending. International financial organizations forecast inflation and economic activity. The list is long but the point is short - forecasting is a fundamental analytic process in every organization. This new tutorial gets you started doing some fundamental time series exploration and visualization.