Recently Discrete Wavelet Transform (DWT) has led to a tremendous surge in many domains of science and engineering. In this study, we present the advantage of DWT to improve time series forecasting precision. This article suggests a novel technique of forecasting by segregating a time series dataset into linear and nonlinear components through DWT. At first, DWT is used to decompose the in-sample training dataset of the time series into linear (detailed) and non-linear (approximate) parts. Then, the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models are used to separately recognize and predict the reconstructed detailed and approximate components, respectively. In this manner, the proposed approach tactically utilizes the unique strengths of DWT, ARIMA, and ANN to improve the forecasting accuracy. Our hybrid method is tested on four real-world time series and its forecasting results are compared with those of ARIMA, ANN, and Zhang's hybrid models. Results clearly show that the proposed method achieves best forecasting accuracies for each series.

Time series analysis is a highly active research topic that encompasses various domains of science, engineering, and finance. A major challenge in this field is to obtain reasonably accurate forecasts of future data from analyzing the past records. A fruitful alternative to using a single forecasting technique is to combine the forecasts from several conceptually different models. Numerous research studies in literature strongly recommend this approach, due to the fact that a combination of multiple forecasts almost always substantially reduces the overall forecasting errors as well as outperforms the component models. In this paper, we propose an ensemble method that selectively combines some of the constituent forecasting models, instead of combining all of them. On each time series, the component models are successively ranked as per their past forecasting accuracies and then we combine the forecasts of a group of high ranked models. Empirical analysis is conducted with nine individual models and four real-world time series datasets. Results clearly show that our proposed ensemble mechanism achieves consistently better accuracies than all component models and other conventional forecasts combination schemes.

No abstract

No abstract