5,426 research outputs found
1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting
Multi-step stock index forecasting is vital in finance for informed
decision-making. Current forecasting methods on this task frequently produce
unsatisfactory results due to the inherent data randomness and instability,
thereby underscoring the demand for advanced forecasting models. Given the
superiority of capsule network (CapsNet) over CNN in various forecasting and
classification tasks, this study investigates the potential of integrating a 1D
CapsNet with an LSTM network for multi-step stock index forecasting. To this
end, a hybrid 1D-CapsNet-LSTM model is introduced, which utilizes a 1D CapsNet
to generate high-level capsules from sequential data and a LSTM network to
capture temporal dependencies. To maintain stochastic dependencies over
different forecasting horizons, a multi-input multi-output (MIMO) strategy is
employed. The model's performance is evaluated on real-world stock market
indices, including S&P 500, DJIA, IXIC, and NYSE, and compared to baseline
models, including LSTM, RNN, and CNN-LSTM, using metrics such as RMSE, MAE,
MAPE, and TIC. The proposed 1D-CapsNet-LSTM model consistently outperforms
baseline models in two key aspects. It exhibits significant reductions in
forecasting errors compared to baseline models. Furthermore, it displays a
slower rate of error increase with lengthening forecast horizons, indicating
increased robustness for multi-step forecasting tasks
A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information
Time series forecasting represents a significant and challenging task across
various fields. Recently, methods based on mode decomposition have dominated
the forecasting of complex time series because of the advantages of capturing
local characteristics and extracting intrinsic modes from data. Unfortunately,
most models fail to capture the implied volatilities that contain significant
information. To enhance the prediction of contemporary diverse and complex time
series, we propose a novel time series forecasting paradigm that integrates
decomposition with the capability to capture the underlying fluctuation
information of the series. In our methodology, we implement the Variational
Mode Decomposition algorithm to decompose the time series into K distinct
sub-modes. Following this decomposition, we apply the Generalized
Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the
volatility information in these sub-modes. Subsequently, both the numerical
data and the volatility information for each sub-mode are harnessed to train a
neural network. This network is adept at predicting the information of the
sub-modes, and we aggregate the predictions of all sub-modes to generate the
final output. By integrating econometric and artificial intelligence methods,
and taking into account both the numerical and volatility information of the
time series, our proposed framework demonstrates superior performance in time
series forecasting, as evidenced by the significant decrease in MSE, RMSE, and
MAPE in our comparative experimental results
Crude oil risk forecasting : new evidence from multiscale analysis approach
Fluctuations in the crude oil price allied to risk have increased significantly over the last decade frequently varying at different risk levels. Although existing models partially predict such variations, so far, they have been unable to predict oil prices accurately in this highly volatile market. The development of an effective, predictive model has therefore become a prime objective of research in this field. Our approach, albeit based in part on previous research, develops an original methodology, in that we have created a risk forecasting model with the ability to predict oil price fluctuations caused by changes in both fundamental and transient risk factors. We achieve this by disintegrating the multi-scale risk-structure of the crude oil market using Variational Mode Decomposition. Normal and transient risk factors are then extracted from the crude oil price using Variational Mode Decomposition and modelled separately using the Quantile Regression Neural Network (QRNN) model. Both risk factors are integrated and ensembled to produce the risk estimates. We then apply our proposed risk forecasting model to predicting future downside risk level in three major crude oil markets, namely the West Taxes Intermediate (WTI), the Brent Market, and the OPEC market. The results demonstrate that our model has the ability to capture downside risk estimates with significantly improved precision, thus reducing estimation errors and increasing forecasting reliability
Improving forecasting accuracy of crude oil price using decomposition ensemble model with reconstruction of IMFs based on ARIMA model
The accuracy of crude oil price forecasting is more important especially for economic development and considered as the lifeblood of the industry. Hence, in this paper, a decomposition-ensemble model with the reconstruction of intrinsic mode functions (IMFs) is proposed for forecasting the crude oil prices based on the well-known autoregressive moving average (ARIMA) model. Essentially, the reconstruction of IMFs enhances the forecasting accuracy of the existing decomposition ensemble models. The proposed methodology works in four steps: decomposition of the complex data into several IMFs using EEMD, reconstruction of IMFs based on order of ARIMA model, prediction of every reconstructed IMF, and finally ensemble the prediction of every IMF for the final output. A case study was carried out using two crude oil prices time series (i.e. Brent and West Texas Intermediate (WTI)). The empirical results exhibited that the reconstruction of IMFs based on order of ARIMA model was adequate and provided the best forecast. In order to check the correctness, robustness and generalizability, simulations were carried out
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