Neural Networks in Business Forecasting Amir Shokri - - PowerPoint PPT Presentation

Neural Networks in Business Forecasting

Amir Shokri Amirsh.nll@gmail.com

Abstract

Neural Network is defined as the ability of a group to solve more problems than its individual members. The idea brings that a group of people can solve problems efficiently and offer greater insight and a better answer than any one individual could provide. The applications of Neural Network enhance an innovative business model for an enterprise. Role of Neural Network in an enterprise brings effectiveness. Further work will be carried

• ut towards the Mathematical modeling of neural networks and various

parameters will be engaged so as to get the required result to desired degree of accuracy.

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Introduction

Forecasting has been dominated by linear methods for many decades. Linear methods are easy to develop and implement and they are also relatively simple to understand and interpret. However, linear models have serious limitation in that they are not able to capture any nonlinear relationships in the data. The approximation of linear models to complicated nonlinear relationships is not always satisfactory. In the early 1980s, Makridakis (1982) organized a large-scale forecasting competition (often called M-competition) where a majority of commonly used linear methods were tested with more than 1,000 real time series data. The mixed results show that no single linear model is globally the best, which may be interpreted as the failure of linear modeling in accounting for a varying degree of nonlinearity that is common in real world problems

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Introduction

networks are mathematical models inspired by the organization and functioning of biological neurons. There are numerous artificial neural network variations that are related to the nature of the task assigned to the

• network. There are also numerous variations in how the neuron is
• modeled. In some cases, these models correspond closely to biological

neurons and in other cases the models depart from biological functioning in significant ways. See the appendix for a more detailed explanation of the artificial neural network paradigm.

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Introduction

In the human brain, a typical neuron collects signal from others through a host of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long thin strand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axiom. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes. An artificial neural network is a simulation of biological brain. The purpose of a neural network is to learn to recognize pattern in our data.

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Introduction

Once the neural network has been trained on sample of our data it can make predictions by detecting similar patterns in future data. Thus a neural network is a computational system inspired by the structure, processing method, learning ability of a biological brain.

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Introduction

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Neural networks

Artificial neural networks (ANNs) are computing models for information processing and pattern identification. They grow out of research interest in modeling biological neural systems, especially human brains. An ANN is a network of many simple computing units called neurons or cells, which are highly interconnected and organized in layers. Each neuron performs the simple task of information processing by converting received inputs into processed outputs. Through the linking arcs among these neurons, knowledge can be generated and stored regarding the strength of the relationship between different nodes. Although the ANN models used in all applications are much simpler than actual neural systems, they are able to perform a variety of tasks and achieve remarkable results.

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Neural networks

Over the last several decades, many types of ANN models have been developed, each aimed at solving different problems. But by far the most widely and successfully used for forecasting has been the feed forward type neural network. Figure 1 shows the architecture of a three-layer feed forward neural network that consists of neurons (circles) organized in three layers: input layer, hidden layer, and output layer. The neurons in the input nodes correspond to the independent or predictor variables (x) that are believed to be useful for forecasting the dependent variable (y) which corresponds to the output neuron. Neurons in the hidden layer are connected to both input and output neurons and are key to learning the pattern in the data and mapping the relationship from input variables to the output variable.

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Neural networks

With nonlinear transfer functions, hidden neurons can process complex information received from input neurons and then send processed information to the output layer for further processing to generate forecasts. In feed forward ANNs, the information flow is one directional from the input layer to the hidden layer then to the output layer without any feedback from the output layer.

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Neural networks

Artificial neural networks (ANN) have been widely touted as solving many forecasting and decision modeling problems . For example, they are argued to be able to model easily any type of parametric or non-parametric process and automatically and optimally transform the input data. These sorts of claims have led to much interest in artificial neural networks. On the other hand, Chatfield (1993) has queried whether artificial neural networks have been oversold or are just a fad. In this paper, we will attempt to give a balanced review of the literature comparing artificial neural networks and statistical techniques. Our review will be segmented into three different application areas: time series forecasting, regression based forecasting, and regression-based decision models.

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Neural networks

Additionally, we will note the literature comparing artificial neural networks and other models such as discriminant analysis. But before that review, we will first examine the general claims made for artificial neural networks that are relevant to forecasting and decision making.

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Neural network as a forecasting tool

Unsupervised learning method creates its own model to interpret the data without known answers. Adaptive resonance theory, Kohonen self-

• rganizing map counter propagation network are some of the popularly

used unsupervised learning approaches. They are often used for clustering

• data. The back propagation algorithm has emerged as one of the most

widely used learning procedures for multilayer networks.

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Neural network as a forecasting tool

A typical back propagation neural network usually has an input layer, some hidden layers and an output layer. The units in the network are connected in a feed forward manner, from the input layer to the output layer. The weights of connections have been given initial values. The error between the predicted output value and the actual value is back propagated through the network for the updating of the weights. This is a supervised learning procedure that attempts to minimize the error between the desired and the predicted outputs.

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Neural network as a forecasting tool

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Neural network as a forecasting tool

There is a marketing application which has been integrated with a neural network system. The Airline Marketing Tactician (a trademark abbreviated as AMT) is a computer system made of various intelligent technologies including expert systems. A feed forward neural network is integrated with the AMT and was trained using back propagation to assist the marketing control of airline seat allocations. The adaptive neural approach was amenable to rule expression. Additionally, the application's environment changed rapidly and constantly, which required a continuously adaptive

• solution. The system is used to monitor and recommend booking advice for

each departure. Such information has a direct impact on the profitability of an airline and can provide a technological advantage for users of the system.

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Neural network as a forecasting tool

While it is significant that neural networks have been applied to this problem, it is also important to see that this intelligent technology can be integrated with expert systems and other approaches to make a functional

• system. Neural networks were used to discover the influence of undefined

interactions by the various variables. While these interactions were not defined, they were used by the neural system to develop useful

• conclusions. It is also noteworthy to see that neural networks can influence

the bottom line.

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Neural network as a forecasting tool

Neural networks change the way to use information in marketing. With such a new information technology, a company using a neural network, will eventually have affordable, near real-time access to all the raw numbers it

• wants. These data may be obtained from consumer credit card applications,

point-of -purchase credit-card sales, and credit agency reports. The real difference among competitors will be the quality of analysis each performs and the capacity of decisions flowing from it. Neural networks help managers gather and process information, such as age, income, credit history, and products purchased.

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Neural network as a forecasting tool

Neural networks have been applied to a wide range of information- processing activities, such as associate memory, pattern classification and clustering, and function approximation and prediction. These applications are characterized by unstructured decision processes, multi objectives and multiple stage decision activities. Such applications may not be efficiently supported using expert and decision support systems technologies.

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Neural network as a forecasting tool

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Forecasting applications of neural networks

The use of ANNs for forecasting has received great attention from many different fields. Given the fact that forecasting problems arise in so many different disciplines and the literature on forecasting with ANNs is scattered in so many diverse fields, it is difficult to cover all neural network forecasting applications. Table 1 provides a sample of recent business forecasting applications with ANNs reported in the literature from 1995 to

• 2003. As can be seen from Table 1, a wide range of business forecasting

problems have been solved by neural networks.

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Forecasting applications of neural networks

Some of these application areas include accounting (forecasting accounting earnings, earnings surprises; predicting bankruptcy and business failure), finance (forecasting stock market movement, indices, return, and risk; exchange rate; futures trading; commodity and option price; mutual fund assets and performance), marketing (forecasting consumer choice, market share, marketing category, and marketing trends), economics (forecasting business cycles, recessions, consumer expenditures, GDP growth, inflation, total industrial production, and US Treasury bond), production and

• perations…

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Forecasting applications of neural networks

… (forecasting electricity demand, motorway traffic, inventory, new product development project success, IT project escalation, product demand or sales, and retail sales), international business (predicting joint venture performance, foreign exchange rate), real estate (forecasting residential construction demand, housing value), tourism and transportation (forecasting tourist, motorway traffic, and international airline passenger volume), and environmental related business (forecasting

• zone level and concentration, air quality.

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Conclusion

Artificial neural networks have emerged as an important tool for business

• forecasting. ANNs have many desired features that are quite suitable for

practical forecasting applications. This chapter provides a general overview

• f the neural networks for forecasting applications. Successful forecasting

application areas of ANNs, as well as critical modeling issues are reviewed. It should be emphasized that each forecasting situation requires a careful study of the problem characteristics, prudent design of modeling strategy, and full consideration of modeling issues. Many rules of thumb in ANNs may not be useful for a new application, although good forecasting principles and established guidelines should be followed.

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Conclusion

ANNs have achieved remarkable successes in the field of business

• forecasting. It is, however, important to note that they may not be a

panacea for every forecasting task under all circumstances. Forecasting competitions suggest that no single method, including neural networks, is universally the best for all types of problems in every situation. Thus, it may be beneficial to combine several different models in improving forecasting

• performance. Indeed, efforts to find better ways to use ANNs for

forecasting should never cease. The subsequent chapters of this book will provide a number of new forecasting applications and address some practical issues in improving ANN forecasting performance.

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Thank You.

A mir S h okri A mirsh .n ll@g mail.com