Abstract
The concept of trend in data and a novel neural network method for the forecasting of upcoming time-series data are proposed in this paper. The proposed method extracts two data sets—the trend and the remainder—resulting in two separate learning sets for training. This method works sufficiently, even when only using a simple recurrent neural network (RNN). The proposed scheme is demonstrated to achieve better performance in selected real-life examples, compared to other averaging-based statistical forecast methods and other recurrent methods, such as long short-term memory (LSTM).
Highlights
In the modern age, a tremendous amount of time-series data, such as stock market fluctuations and average temperature per month per region, are generated and saved with each passing second.This trend is accelerating, showing no signs of slowing and, many tools have been developed and used to extract useful information from such data for various purposes, such as for profit, estimation, detection, and future prediction
The aim of this paper is to provide a new technique for the prediction of more accurate future trends, using a single-layer recurrent neural network (RNN) structure for a given time-series
We introduced a new algorithm to predict future data from given time-series data by finding trends in the data set which fit into the given data and, applying existing simple recurrent neural network learning to them
Summary
A tremendous amount of time-series data, such as stock market fluctuations and average temperature per month per region, are generated and saved with each passing second This trend is accelerating, showing no signs of slowing and, many tools have been developed and used to extract useful information from such data for various purposes, such as for profit, estimation, detection, and future prediction. Symmetry 2019, 11, 912 emphasize the strength of trend methods; that is, even with one of the simplest learning schemes, the forecasting results are better than some variance of RNN learning methods, which is a motivation and strength of this work This is demonstrated by the examples and numerical experiments, in which the use of both trend and non-trend data parts results in better prediction performance.
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