Singular spectral analysis (SSA) is a non-parametric method used in the prediction of non-stationary time series. It has two parameters, which are difficult to determine and very sensitive to their values. Since, SSA is a deterministic-based method, it does not give good results when the time series is contaminated with a high noise level and correlated noise. Therefore, we introduce a novel method to handle these problems. It is based on the prediction of non-decimated wavelet (NDW) signals by SSA and then, prediction of residuals by wavelet regression. The advantages of our method are the automatic determination of parameters and taking account of the stochastic structure of time series. As shown through the simulated and real data, we obtain better results than SSA, a non-parametric wavelet regression method and Holt–Winters method.

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We introduce a wavelet characterization of continuous‐time periodically correlated processes based on a linear combination of infinite‐dimensional stationary processes. The finite version of this linear combination converges to the main process. The first‐order and second‐order estimators based on the wavelets are presented. Under a simple and easy algorithm, the periodically correlated process is simulated for a given autocovariance function. The proposed algorithm has two main advantages: first, it is fast, and second, it is distribution free. We indicate through four examples that the simulated data are periodically correlated with the desired period.

Purpose The Islamic financial system consists of three functional areas of Islamic banking, Islamic insurance and the Islamic capital market, which, with its development and progress, has become an important part of the global financial market and as an alternative, efficient financial model in front of the financial system. Takaful insurance is a relatively new but growing sector of the Islamic financial industry. Today, this insurance has attracted the attention of many researchers and executives. To further improve, it is important to identify the key factors according to the demand and also to evaluate their importance. In Iran’s insurance industry, Takaful product has not yet been released, and insurers have recently entered this field. Therefore, the purpose of this paper, an attempt has been made to identify the factors affecting the demand for Takaful insurance in Iran. The results of this research can be useful for policymakers and Takaful providers in formulating appropriate strategies to increase the demand for Takaful insurance. Design/methodology/approach In this regard, in this research, using library studies, indicators affecting demand were identified. After that, using a field study and distributing a questionnaire among experts in the field of research, the importance of the indicators was analyzed relative to each other and the indicators were ranked based on their importance. Findings Based on the results, the indicators are divided into five categories of economic, social, demographic, marketing and sales and features of Takaful insurance products. Originality/value To the best of the authors’ knowledge, this is the first study regarding the identification and ranking of factors affecting the demand for Takaful insurance in Iran’s insurance industry.

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