A novel distance-based moving average model for improvement in the predictive accuracy of financial time series

Time-series forecasting is essential for system analysis. Many traditional studies have paid attention to individual stock-oriented solutions and disregarded general approaches on financial time series or skipped the dynamics of the system and its triggering components. It is difficult to fully adapt to evolving market conditions with stable financial indicators. For this reason, the proposed novel distance-based exponential moving-average (DBEMA) model is dynamically designed to overcome the changing conditions of financial time series. A novel distance-based moving-average feature model can produce an adaptive prediction approach for financial time series. To evaluate the impact of the novel proposed DBEMA features, they are compared to the features selected by recursive feature elimination using classification and regression trees among the financial indicators, using benchmark classification models. To confirm the performance of the proposed novel distance-based moving-average features, the forecasting results of the features are compared using linear regression, bagged trees regressor, Gaussian naive Bayes, k-nearest neighbors, random forests, multilayer perceptron, convolutional neural network, long short-term memory, gated recurrent unit, and relative strength index method benchmark models. The experimental analysis has shown that methods with our proposed novel DBEMA features has better forecasting accuracy with respect to the methods without DBEMA. Therefore, the proposed novel distance-based moving-average methodology designed for financial time-series analysis demonstrates that it guides a new perspective in nonlinear time-series trends.