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

Yaacob, Nurul Aityqah; Jaber, Jamil J.; Pathmanathan, Dharini; Alwadi, Sadam; Mohamed, Ibrahim

doi:10.3390/math9182295

Cited by 6 publications

(6 citation statements)

References 19 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MODWT stands as a modification of the traditional DWT, embodying a time shift-invariant methodology, as noted by (AL-Musaylh et al, 2020). This temporal shift invariance ensures that alterations in the signal induce analogous changes in the wavelet coefficients, emphasizing the method's consistency and reliability in capturing temporal variations within the signal ( (Yaacob et al, 2021); (P. Xu et al, 2022)).…”

Section: Maximum Overlap Discrete Wavelet Transform (Modwt)mentioning

confidence: 99%

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Poonam Chhabra,

Rosy Madaan

2023

J. Adv. Zool.

View full text Add to dashboard Cite

An electrocardiogram (ECG) is a continuous electrical signal from the heart that is recorded to understand the activity and condition of the heart. A recorded ECG signal always follows a defined pattern for a normal heart condition. Variation in the normal ECG pattern can be seen in cases of numerous cardiac abnormalities. A recorded ECG is also affected by a number of noises and distortions, resulting in a low SNR. A variation in ECG pattern can lead to incorrect study and improper diagnosis of heart condition. Thus, to perform an efficient analysis, it is necessary to preprocess the ECG waveform. ECG preprocessing requires noise removal and analysis of necessary features needed to study cardiac activity. In this paper, ECG preprocessing is evaluated by using two noise removal techniques, i.e., finite and infinite impulse response. After this, the R-peaks are detected using discrete wavelet transform (DWT), maximal optimal DWT, principal component analysis and independent component analysis. A wavelet transform technique is further proposed using Savitzky-Golay filtering and DWT. The results obtained from the proposed methodology represent the best results compared to those of other methods explicated in this paper.

show abstract

Section: Maximum Overlap Discrete Wavelet Transform (Modwt)mentioning

confidence: 99%

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Poonam Chhabra,

Rosy Madaan

2023

J. Adv. Zool.

View full text Add to dashboard Cite

show abstract

“…While WT localizes in both the frequency (scale) and time (position) domain, FT only localizes in the frequency domain and not the time domain. The original time series data are transformed using the mathematical function WT into a time-scale domain [32]. The WT transforms the period (or frequency) of data without affecting time resolution.…”

Section: Modwt Modelmentioning

confidence: 99%

“…where φ(f) is the FT that is a function of frequency f, φ(t). The applications of WT are image analysis and signal processing [32]. It overcomes the problem of FT, especially when dealing with time, space, or frequency.…”

Section: Plos Onementioning

confidence: 99%

Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting

et al. 2022

Self Cite

View full text Add to dashboard Cite

This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Saudi Arabia’s stock exchange (Tadawul) data. The MODWT comprises five mathematical functions and fuzzy inference rules. The inputs are the oil price (Loil) and repo rate (Repo) according to multiple regression correlation, and the Engle and Granger Causality test Engle RF, (1987). The logarithm of the stock market price (LSCS) in Tadawul reflects the output variable. The correlation matrix reveals that there is no collinearity between the input variables, and the causality test demonstrates that the input variables significantly influence the outcome variable. According to the multiple regression, there is a substantial negative influence between Loil and LSCS but a significant positive effect between Repo and output. For the 80% dataset under ME (0.000005), MAE (0.003214), and MAPE (0.064497), the MODWT-LA8 (ARIMA(1,1,0) with drift) for the LSCS variable performs better than other WT functions. In the novel hybrid model MODWT-FIR.DM, each function’s approximation coefficient (LSCS) is applied with input variables (Loil and Repo). We evaluate the performance of the proposed model (MODWT-LA8-FIR.DM) using different statistical measures (ME, RMSE, MAE, MPE) and compare it to two established models: the original FIR.DM and other MODWT-FIR.DM functions for forecasting 20% of datasets. The outcomes show that the MODWT-LA8-FIR.DM performs better than the traditional models based on lower ME (3.167586), RMSE (3.167638), MAE (3.167586), and MPE (80.860849). The proposed hybrid model may be a potential stock market forecasting model.

show abstract

“…The MODWT has been shown to cite faulty signal attributes in cross-country cases and growing errors, which are complex. Furthermore, Yaacob et al (2021) have researched the application of employing discrete wavelet transform filters, a combination of maximum overlap, modeling, and forecasting the time-dependent mortality index of the Lee-Carter model. Yousuf's research Yousuf et al (2021) integrates the MODWT with the ARIMA and adjusts the moving window MC.…”

Section: A Introductionmentioning

confidence: 99%

The MODWT-ARIMA Model in Forecasting The COVID-19 Cases

Imro'ah,

Huda

2023

JTAM

View full text Add to dashboard Cite

The Maximal Overlap Discrete Wavelet Transform-Autoregressive Integrated Moving Average (MODWT-ARIMA) is a forecasting method that uses the ARIMA model generated from MODWT data. The purpose of this study is to analyze an investigation into the MODWT-ARIMA model with regard to the total number of COVID-19 cases in DKI Jakarta. For this study, daily data on cases of Covid-19COVID-19 in DKI Jakarta were obtained. The model is trained with data from April 3, 2022, to June 11, 2022 (referred to as the "in-sample"), and the outcomes of the prediction are tested with data from June 12, 2022, to June 18, 2022 (referred to as the "out-sample"). These data exhibit trends and are organizedorganised into four data series using MODWT. The ARIMA modelling technique is applied to each of the produced sequences. When using the MODWT-ARIMA approach, the RMSE value obtained from the in-sample data is found to be lower than the RMSE value obtained from the out-sample data. In light of the findings, Iit t became clear that MODWT-ARIMA is better suited for estimation than prediction. The fact that the RMSE value for the data acquired from the in-sample is lower than the RMSE value for the data collected from the out-sample demonstrates that this is the case.

show abstract

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

Cited by 6 publications

References 19 publications

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting

The MODWT-ARIMA Model in Forecasting The COVID-19 Cases

Contact Info

Product

Resources

About