Are CDS spreads predictable during the Covid-19 pandemic? Forecasting based on SVM, GMDH, LSTM and Markov switching autoregression.
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ABSTRACT:
SUBMITTER: Vukovic DB
PROVIDER: S-EPMC8782769 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed.ResultsThe results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand's data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19.ConclusionThe forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.
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Project description:Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.
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Project description:Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.
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