EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

Hybrid Lee-Carter model with adaptive network of fuzzy inference system and wavelet functions

Mortality studies are essential in determining the health status and demographic composition of a population. The Lee–Carter (LC) modelling framework is extended to incorporate the macroeconomic variables that affect mortality, especially in forecasting. This paper makes several major contributions. First, a new model (LC-WT-ANFIS) employing the adaptive network-based fuzzy inference system (ANFIS) was proposed in conjunction with a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) that includes five mathematical functions, namely, Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6) to enhance the forecasting accuracy of the LC model. Annual mortality data was collected from five countries (Australia, England, France, Japan, and the USA) from 1950 to 2016. Second, we selected gross domestic product (GDP), unemployment rate (UR), and inflation rate (IF) as input values according to correlation and multiple regressions. The input variables in this study were obtained from the World Bank and Datastream. The output variable was collected from the mortality rates in Human Mortality Database. Finally, the LC model’s projected log of death rates was compared with wavelet filters and the traditional LC model. The performance of the proposed model (LC-WT-ANFIS) was evaluated based on mean absolute percentage error (MAPE) and mean error (ME). Results showed that the LC-WT-ANFIS model performed better than the traditional model. Therefore, the proposed forecasting model is capable of projecting mortality rates

Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the ARIMA model is enhanced with the integration of various maximal overlap discrete wavelet transform filters such as the least asymmetric, best-localized, and Coiflet filters. These models are then applied to the mortality data of Australia, England, France, Japan, and USA. The accuracy of the projecting log of death rates of the MODWT-ARIMA model with the aforementioned wavelet filters are assessed using mean absolute error, mean absolute percentage error, and mean absolute scaled error. The MODWT-ARIMA (5,1,0) model with the BL14 filter gives the best fit to the log of death rates data for males, females, and total population, for all five countries studied. Implementing the MODWT leads towards improvement in the performance of the standard framework of the LC model in forecasting mortality rates