Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and Cl benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research. (C) 2011 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

Learning in a large square economy

Learning is introduced into a sequence of large square endowment economies indexed by n, in which agents live n periods. Young agents need to forecast n - 1 periods ahead in these models in order to make consumption decisions, and thus these models constitute multi-step ahead systems. Real time learning is introduced via least squares. The systems studied in this paper are sometimes locally convergent when n = 2,3 but are never locally convergent when . Because the economies studied are analogous, nonconvergence can be attributed solely to the multi-step ahead nature of the forecast problem faced by the agents. We interpret this result as suggesting that beliefs-outcomes interaction may be an important element in explaining actual dynamics in general equilibrium systems of this type.Consumption (Economics)

TSFDC: A Trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market’s trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. We argue that TSFDC outperforms another DC-based trading strategy

Church Population Growth Prediction Using Predictive Analytic (Linear Regression Model) Technique

The world today has become highly technological and Information and Communication Technology (ICT) has been identified as one of the basic pillars on which the modern society stands. In Nigeria today, competency in basic ICT skills is now regarded as one of the basic requirements for our day-day activities in religion, economic, financial and political. The use of available ICT tools equally enhances the validity of results and assist management in planning and budgeting for growth. The church cannot be an exception in this regard as “spreading the gospel” is the goal of the churches by growing the number of new members using available tools. The issue of church growth transcends many areas of church activities and the application of the ICT infrastructures could be gainfully put into use in some of these areas for greater impact on church development. The difficulties confronting church organisations in respect of church growth are becoming unsurmountable for those that have refused to embrace and deploy Information and Communication Technologies to enhance growth and its resultant complexities. In this paper, a system capable of predicting church growth with the application of Linear Regression Model (LRM) using the historic data of a growing church is presented. The results demonstrated the importance of predicting the population of organisations such as religious centers that often have a growing teeming population of members. Such results of prediction using ICT tools would be an essential guide in the budgeting requirements of such organisations. Keywords: Prediction, growth, tools, ICT, normalize, linear, budgeting DOI: 10.7176/CEIS/12-1-05 Publication date: January 31st 202

A Vector-Based Approach to Virtual Machine Arrangement

Cloud based data centres benefit from minimizing operating costs and service level agreement violations. Vector-based data centre management policies have been shown to assist with these goals. Vector-based data centre management policies arrange virtual machines in a data centre to minimize the number of hosts being used which translates to greater power efficiency and reduced costs for the data centre overall. I propose an improved vector-based virtual machine arrangement algorithm with two novel additions, namely a technique that changes what it means for a host to be balanced and a concept that excludes undesirable target hosts, thereby improving the arrangement process. Experiments conducted with a simulated data centre demonstrate the effectiveness of this algorithm and compares it to existing algorithms