Forecasting mortality rate by multivariate singular spectrum analysis

Mahmoudvand, Rahim; Konstantinides, Dimitrios G.; Rodrigues, Paulo Canas

doi:10.1002/asmb.2274

Cited by 27 publications

(16 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy performance of the proposed method is evaluated using integrated square error (ISE), one of the most popular error measurements for non-parametric estimation, which is formulated in Eq. (15) [35], as follows:…”

Section: Methodsmentioning

confidence: 99%

Stock Price Forecasting Using Univariate Singular Spectral Analysis through Hadamard Transform

Syukur¹,

Marjuni²

2020

IJIES

View full text Add to dashboard Cite

The stock price prediction or forecasting is one of the important information in the stock market. Investors need stock price information before buying shares, while shareholders need stock price information for selling shares. There are many stock price prediction techniques have been proposed in the time series analysis. One of the simplest and powerful techniques is singular spectrum analysis, which works on the time series decomposition that is constructed using sub-series or window of the initial time series. However, choosing the exact window length is not easy because it depends on the time series characteristics. In this paper, the Hadamard transform of time series is proposed as an alternative technique to choose the window length in time series embedding. Technically, the length of the window for time series embedding is determined directly based on the size of the Hadamard spectrum. The experimental results show that the proposed method not only facilitates the determination of window length in time series embedding but can also improve the performance of the standard singular spectrum analysis method. The error rates of the proposed and the baseline methods (the standard singular spectrum analysis and the standard singular spectrum analysis with minimum description length) are 0.0088, 0.0194, and 0.1441, respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

Stock Price Forecasting Using Univariate Singular Spectral Analysis through Hadamard Transform

Syukur¹,

Marjuni²

2020

IJIES

View full text Add to dashboard Cite

show abstract

“…Its main aim is to decompose the original time series into a set of components that can be interpreted as trend components, seasonal components, and noise components [ 3 , 4 , 5 , 6 ]. SSA has proven both wide usefulness and applicability across many applications [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], being that its scope of application ranges from parameter estimation to time series filtering, synchronization analysis, and forecasting [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Rodrigues

Pimentel

Messala

et al. 2020

Entropy

Self Cite

View full text Add to dashboard Cite

Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noise-free time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and non-parametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models.

show abstract

“…Golyandina et al 1 and Golyandina and Zhigljavsky 2 provide a comprehensive overview of SSA, and many studies have proven its superiority over traditional methods for time series analysis. [3][4][5] The scope of application of the methodology is also wide with SSA finding good use in areas such as industry, [6][7][8] demography, 9,10 economics and finance, [11][12][13] material and signal processing, 14,15 climatology, 16,17 neural networks, 18 and nuclear science. 19 The primary goal of SSA is to decompose the original (univariate) time series into a sum of a small number of interpretable components such as trend, periodical components and noise components.…”

Section: Introductionmentioning

confidence: 99%

A robust approach to singular spectrum analysis

Rodrigues

Lourenço

Mahmoudvand

2018

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

Singular spectrum analysis (SSA) is a nonparametric method for time series analysis and forecasting that incorporates elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems, and signal processing. Although this technique has shown to be advantageous over traditional model‐based methods, in particular, one of the steps of the SSA algorithm, which refers to the singular value decomposition (SVD) of the trajectory matrix, is highly sensitive to data contamination. Specifically, and because SVD decomposition is least squares based, the presence of a single outlier, if extreme, may be enough to draw the leading principal component towards itself resulting in possible misinterpretations, which may subsequently, in particular and in the case of time series analysis, lead to reduced quality of model fit and forecast accuracy. In order to alleviate this problem, a robust SSA algorithm is proposed, where a robust SVD procedure replaces the least squares based one in the original SSA procedure. The SSA and the robust SSA approaches are compared in terms of model fit quality via Monte Carlo simulations that contemplate both clean and noisy/contaminated time series. The adequacy and value of the proposed approach is then compared with the standard SSA using real data about the industrial production index.

show abstract

Forecasting mortality rate by multivariate singular spectrum analysis

Cited by 27 publications

References 22 publications

Stock Price Forecasting Using Univariate Singular Spectral Analysis through Hadamard Transform

Stock Price Forecasting Using Univariate Singular Spectral Analysis through Hadamard Transform

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

A robust approach to singular spectrum analysis

Contact Info

Product

Resources

About