A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting

In this study, a new bootstrapped hybrid artificial neural network is proposed for forecasting. This new neural network provides input significance, linearity and nonlinearity hypothesis tests in a unique network structure via a residual bootstrap approach. The network has three parts: linear, non-linear and a combination with associated weights and biases. These weights are used to test the input significance, linearity and nonlinearity hypotheses with this new method providing empirical distributions for forecasts and weights. The proposed method employs a bagging approach to obtain forecasts. It is then applied to real-time series including the M4 Competition data set and stock exchange time series where its performance is compared with appropriate benchmark methods including other popular neural networks. The proposed method results are less affected than other neural networks by initial random weights, which means that the results of the proposed method are more stable and precise. The new method provides improvements in forecasting accuracy over the established benchmarks

Special Issue: 11th International Statistics Days Conference

[No abstract available

A novel seasonal fuzzy time series method

Aladag, Cagdas Hakan/0000-0002-3953-7601; Egrioglu, Erol/0000-0003-4301-4149WOS: 000312412800005Fuzzy time series forecasting methods, which have been widely studied in recent years, do not require constraints as found in conventional approaches. On the other hand, most of the time series encountered in real life should be considered as fuzzy time series due to the vagueness that they contain. Although numerous methods have been proposed for the analysis of time series in the literature, these methods fail to forecast seasonal fuzzy time series. The limited number of seasonal fuzzy time series methods consider only the fuzzy set having the highest membership value, rather than the membership value of observations belonging to each fuzzy set. This is contrary to fuzzy set theory and causes information loss, thus affecting forecasting performance negatively. In this study, a new seasonal fuzzy time series method which considers the membership value of the observations belonging to each set in both forecasting fuzzy relations and in the defuzzification step is proposed. The proposed method is applied to a real seasonal time series