Journal of Statistics: Advances in Theory and ApplicationsAuthor's: Tsung-Lin Li and Chen-An Tsai
Pages: [27] - [50]
Received Date: February 18, 2021
Submitted by:
DOI: http://dx.doi.org/10.18642/jsata_7100122182
Time series forecasting is a challenging task of interest in many
disciplines. A variety of techniques have been developed to deal with
the problem through a combination of different disciplines. Although
various researches have proved successful for hybrid models, none of
them carried out the comparisons with solid statistical test.
This paper proposes a new stepwise model determination method for
artificial neural network (ANN) and a novel hybrid model combining
autoregressive integrated moving average (ARIMA) model, ANN and
discrete wavelet transformation (DWT). Simulation studies are
conducted to compare the performance of different models, including
ARIMA, ANN, ARIMA-ANN, DWT-ARIMA-ANN and the proposed method,
ARIMA-DWT-ANN. Also, two real data sets, Lynx data and cabbage data,
are used to demonstrate the applications. Our proposed method,
ARIMA-DWT-ANN, outperforms other methods in both simulated datasets
and Lynx data, while ANN shows a better performance in the cabbage
data. We conducted a two-way ANOVA test to compare the performances of
methods. The results showed a significant difference between
methods.
As a brief conclusion, it is suggested to try on ANN and ARIMA-DWT-ANN
due to their robustness and high accuracy. Since the performance of
hybrid models may vary across data sets based on their ARIMA alike or
ANN alike natures, they should all be considered when encountering a
new data to reach an optimal performance.
time series forecasting, hybrid model, autoregressive moving average model, artificial neural network (ANN), discrete wavelet transformation (DWT).
