In this study, a novel forecasting model based on Wavelet Neural Network (WNN) is proposed to predict monthly crude oil spot prices. In this new model, OECD industrial petroleum inventory levels are used as the independent variable, and Wavelet Neural Network (WNN) is used to model the nonlinear relationship between inventories and prices. For verification purpose, West Texas Intermediate (WTI) crude oil spot price is used. Experiment results reveal that WNN can model the nonlinear relationship between inventories and price very well. Furthermore, Read the rest of this entry »
One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of volatility accurately. While wavelet function yields features that describe of the volatility time series both at various locations and at varying time granularities, so this paper construct a multidimensional wavelet kernel function and prove it meeting the mercer condition to address this problem. The applicability and validity Read the rest of this entry »
The main theme of this article is the mathematical theory needed to develop a computer application that helps to predict economic data, which contain an element of time. This is the case of stock markets, currency exchange rates, inflation rates, etc. In the second part of this paper an example application and its forecasting results are described. The application was used to predict Read the rest of this entry »
This paper presents a set of tools, which allow gathering information about the frequency components of a time-series. We focus on the concepts rather than giving too much weight to mathematical technicalities. In a first step, we discuss spectral analysis and filtering methods. Spectral analysis can be used to identify and to quantify the different frequency components of a data series. Filters permit to capture specific components (e.g. trends, cycles, seasonalities) of the original timeseries. Both spectral analysis and standard filtering methods have two main drawbacks: (i) they impose strong restrictions regarding the possible processes underlying the dynamics of the series (e.g. stationarity), and, (ii) they lead to a pure frequency-domain representation of the data, i.e. all information from the time-domain representation is lost in the operation. In a second step, we introduce wavelets Read the rest of this entry »
