Improving forecasting accuracy for stock market data using EMD-HW bagging

Awajan, Ahmad M.; Ismail, Mohd Tahir; Wadi, S. Al

doi:10.1371/journal.pone.0199582

Cited by 34 publications

(15 citation statements)

References 47 publications

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“…The Holt-Winter forecasting method is an extension of exponential smoothing and applied for univariate time series [ 8 ]. This method doesn’t need a high data storage and is simple [ 11 ]. The HW is suitable for short-term forecasting and uses the maximum likelihood function for estimating parameters [ 8 , 11 ].…”

Section: Methodsmentioning

confidence: 99%

“…This method doesn’t need a high data storage and is simple [ 11 ]. The HW is suitable for short-term forecasting and uses the maximum likelihood function for estimating parameters [ 8 , 11 ]. There are two Holt-Winter models that use additive or multiplicative models based on the seasonal component [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

“…The HW is suitable for short-term forecasting and uses the maximum likelihood function for estimating parameters [ 8 , 11 ]. There are two Holt-Winter models that use additive or multiplicative models based on the seasonal component [ 11 ]. The additive models are applied for a model with a linear trend and with an exponential trend.…”

Section: Methodsmentioning

confidence: 99%

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Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Talkhi

Fatemi

Amirghofran

et al. 2021

Biomedical Signal Processing and Control

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Background The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. Methods In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. Results Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2,484 new confirmed and 114 new death cases of COVID-19. Conclusion According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Talkhi

Fatemi

Amirghofran

et al. 2021

Biomedical Signal Processing and Control

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“…Bagging does not necessarily improve forecast accuracy in all cases. Nevertheless, this method and its derivatives tend to outperform traditional forecasting procedures [37].…”

Section: Baggingmentioning

confidence: 99%

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

Ornella¹,

Kruseman²,

Crossa³

2020

Drought - Detection and Solutions

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Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers.

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“…This suggests that no single model is suitable for forecasting the returns of all stock markets. Awajan et al (2018) compared the performance of several forecasting methods by applying them to six stock markets and found that the empirical mode decomposition Holt-Winters method (EMD-HW) provided more accurate forecasts than other models.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of forecasting methods from selected stock market returns

Mallikarjuna

Rao

2019

Financ Innov

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Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. However, there have been very few studies of groups of stock markets or indices. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets. In this context, this study aimed to examine the predictive performance of linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to find an appropriate model to forecast the stock returns of developed, emerging, and frontier markets. We considered the daily stock market returns of selected indices from developed, emerging, and frontier markets for the period 2000-2018 to evaluate the predictive performance of the above models. The results showed that no single model out of the five models could be applied uniformly to all markets. However, traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

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Improving forecasting accuracy for stock market data using EMD-HW bagging

Cited by 34 publications

References 47 publications

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

Evaluation of forecasting methods from selected stock market returns

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