Traditionally analysts in retail, manufacturing and many other industries use a variety of statistical methods to solve a range of problems in forecasting, data classification and pattern recognition. Some of these methods include regression analysis, logistic regression, survival and reliability analysis and Auto-Regressive Integrated Moving Average (ARIMA) modeling. However, because each of these methods uses different software algorithms with different data assumptions, forecasters must learn to use an assortment of tools to solve problems and produce answers.
Fortunately, neural networks can replace all of these methods and produce forecasts as accurate as or better than those available from other statistical methods. In fact, neural networks offer many advantages, including: improved accuracy over traditional statistical methods; a unified approach to a wide variety of predictive analytics problems; and they requires fewer statistical assumptions and can manage complex predictive analytics tasks in a more automated way, which saves time for analysts and programmers. We’ll take a look at what neural networks are, and why they’re suited for certain kinds of analytics, particularly predictive analytics.
How Neural Networks Work
Predictive analytics, pattern recognition and classification problems are not new. They existed years before the commercial application of neural network solutions in the 1980s. In reality, neural networks were discovered much earlier. McCulloch and Pitts wrote one of the first published works on artificial neural networks in 1943. In their paper, they describe a threshold neuron as a model for how the human brain stores and processes information. Neural networks were designed to mimic how the brain learns and analyzes information. For years following their paper, interest in the McCulloch-Pitts neural network was limited to theoretical discussions, until now.
Among many other benefits, applying these artificial neural networks to predictive analytics provides analysts with a single framework for solving so many traditional problems and, in some cases, extends the range of problems that can be solved. Once trained to learn, a neural network is much more efficient and accurate in circumstances where complex predictive analytics is required. This is because, just like our brains, neural networks are composed of a series of interconnected calculating nodes that are designed to map a set of inputs into one or more output signals. The nodes are referred to as perceptrons.
In many cases, simple neural network configurations yield the same solution as many traditional statistical applications. For example, a single-layer, feed-forward neural network with linear activation for its output perceptron is equivalent to a general linear regression fit.
Although neural network solutions for predictive analytics, pattern recognition and classification problems can be very different, they are always the result of computations that proceed from the network inputs to the network outputs. The network inputs are referred to as patterns, and outputs are referred to as classes. Frequently the flow of these computations is in one direction, from the network input patterns to its outputs. Networks with forward-only flow are referred to as feed-forward networks.
Figure 1: A Two-layer, Feed-Forward Network with Four Inputs and Two Outputs
Other networks, such as recurrent neural networks, allow data and information to flow in both directions.
Figure 2: A Recurrent Neural Network with Four Inputs and Two Outputs
