Abstract
In this work, are developed an experimental computer program in Matlab language version 7.1 from the univariate method for time series forecasting called Theta, and implementation of resampling technique known as computer intensive bootstrap to estimate the prediction for the point forecast obtained by this method by confidence interval. To solve this problem built up an algorithm that uses Monte Carlo simulation to obtain the interval estimation for forecasts. The Theta model presented in this work was very efficient in M3 Makridakis competition, where tested 3003 series. It is based on the concept of modifying the local curvature of the time series obtained by a coefficient theta (Θ). In it's simplest approach the time series is decomposed into two lines theta representing terms of long term and short term. The prediction is made by combining the forecast obtained by fitting lines obtained with the theta decomposition. The results of Mape's error obtained for the estimates confirm the favorable results to the method of M3 competition being a good alternative for time series forecast.
Highlights
The forecasting models are of great importance in the academic and social media because of its wide applicability in various areas of scientific, industrial, commercial and services
The techniques of time series forecasting are the predictions from sequences of past values, in other words, from observations of the series [Zt-1, Zt-2, Zt-3, .... , Z1]
Theta Model is decomposed into two theta lines, L (Θ = 0) and L (Θ = 2) and extrapolated by linear trend and simple exponential smoothing, respectively
Summary
The forecasting models are of great importance in the academic and social media because of its wide applicability in various areas of scientific, industrial, commercial and services. These predictions, which are made by such companies to estimate the demand for its products, and plan the production schedule, shopping and other activities including identifying when and where to focus marketing efforts (LIEBEL, 2004). The techniques of time series forecasting are the predictions from sequences of past values, in other words, from observations of the series [Zt-1, Zt-2, Zt-3, .... The objectives of the techniques of time series forecasting are:
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