Short-term Predictive Research Articles

Short-term thermal parameter prediction of building energy system based on MIE-JANET method

The precise prediction of thermal parameters in building energy systems is foundational for the efficient and stable operation of the system, aiding in the formulation of optimal building operation strategies. Currently, in the field of architecture, there is a lack of parameter prediction for thermal parameters that involve multiple devices and thermal processes. Moreover, the widely used long short-term memory (LSTM) algorithm in this field tends to have a large number of network parameters, leading to longer training times. Aiming at these problems, a short-term predictive method for thermal parameters named MIE-JANET is proposed. Mutual information entropy was employed to select relevant features from system operation data. Multiple short-term prediction models were constructed by JANET mdoel. The results indicate the superior predictive performance compared to similar algorithms. When considering insufficient data, the mean absolute error for the JANET decreases by 4.9% relative to LSTM. With the same structure, the training time of the JANET model was reduced by more than 40% compared to LSTM. Moreover, The accuracy of the room temperature model, return water temperature model, and supply water temperature model has significantly improved compared to the physical model. This approach provides a foundational model for the intelligent control of building energy systems.

A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market

In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm and wide use of deep-learning methodologies, the variants of long-short-term-memory (LSTM) networks such as Vanilla-LSTM, Stacked-LSTM, Bidirectional-LSTM, CNN-LSTM, and Conv-LSTM are used to build effective investing trading systems for both short-term and long-term timeframes. Then, a deep network-based system used to obtain the trends (up trends and down trends) of the predicted closing price of the currency pairs is proposed based on the best fit predictive networks measured using a few performance measures and Friedman’s non-parametric tests. The observed trends are compared and validated with a few readily available technical indicators such as average directional index (ADX), rate of change (ROC), momentum, commodity channel index (CCI), and moving average convergence divergence (MACD). The predictive ability of the proposed strategy for trend analysis can be summarized as follows: (a) with respect to the previous day for short-term predictions, AUD:INR achieves 99.7265% and GBP:INR achieves 99.6582% for long-term predictions; (b) considering the trend analysis strategy with respect to the determinant day, AUD:INR achieves 98.2906% for short-term predictive days and USD:INR achieves an accuracy of trend forecasting with 96.0342%. The significant outcome of this article is the proposed trend forecasting methodology. An attempt has been made to provide an environment to understand the average, maximum, and minimum unit up and/or downs observed during trend forecasting. In turn, this deep learning-based strategy will help investors and traders to comprehend the entry and exit points of this financial market.

Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients’ data using machine learning - Based on the ARCTIC study

ObjectiveThe ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of exacerbations. MethodsData from 29,396 asthma patients was collected from electronic medical records and national registers covering clinical and epidemiological factors (e.g. comorbidities, health care contacts), between 2000 and 2013. Machine-learning classifiers were used to create models to predict exacerbations within the next 15 days. Model selection was done using the mean cross validation score of area under precision-recall curve (AUPRC). ResultsThe most important predictors of exacerbation were comorbidity burden and previous exacerbations. Model validation on test data yielded an AUPRC = 0.007 (95% CI: ± 0.0002), indicating that historic clinical information alone may not be sufficient to predict a near future risk of asthma exacerbation. ConclusionsSupplementation with additional data on environmental triggers, (e.g. weather, pollen count, air quality) and from wearables, might be necessary to improve performance of the short-term predictive model to develop a more clinically useful tool.

Short-Term Predictive Power Management of PV-Powered Nanogrids

Optimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power and EV charging/discharging power is important. Unlike the PV power used ahead of costly grid power and entirely dependent on weather condition, timing of EV charging/discharging depends on power management scheme. In order to find out the timing for EV charging/discharging, short-term prediction of PV power production is considered as a key contributor. When PV power production is predicted to decrease in short-term, e.g., 10minutes, discharging power of EVs can compensate the loss and, when predicted to increase in short-term, EVs are charged to capitalize on the gain. Short-term prediction of PV power production is performed by long short-term memory (LSTM) network trained and validated by dataset of PV power production over 1 year. In addition, variation of outdoor temperature in relation to indoor temperature is factored in to determine the timing for EV charging/discharging. Our work is comprehensive in that various electric appliances as well as PV source and EVs are taken into account for power management of nanogrid. Simulation results show the cost benefit obtained from the short-term prediction of PV power production and consequent EV charging/discharging while managing peak demand below maximum allowed level.

Risk assessment of imminent violence in acute psychiatry: a step towards an extended model

ABSTRACTAggression occurs frequently in mental health care settings, and studies have reported that 17% to 31% of patients admitted to acute psychiatric wards commit violence. Inpatients’ fluctuating mental states and behaviour patterns reinforce the need for an assessment instrument to predict potential violence in a timely manner. This naturalistic prospective inpatient study investigated whether an extended short-term risk assessment model that combines (a) short-term risk assessment with the Broset Violence Checklist (BVC), (b) patient’s own prediction of violence with the Self-Report Risk Scale (SRS) and (c) single items from the Violence Risk Screening 10 (V-RISK-10) provides better short-term predictive accuracy for violence than the BVC alone. All patients admitted to a psychiatric emergency hospital in Norway during one year were included (N = 508). Stepwise multivariate generalised linear mixed model analyses were conducted. When adjusting for repeated measurements, the results indicated that an extended model for short-term risk assessment, consisting of the BVC, SRS and Item 2 Previous and/or current threats from the V-RISK-10 explained more variance of imminent violence, compared to the BVC alone. Further studies are recommended to investigate whether the extended model provides a clinically better short-term risk prediction of imminent violence, compared to the BVC alone.

News and social media emotions in the commodity market

PurposeEmotion plays a significant role in both institutional and individual investors’ decision-making process. Emotions affect the perception of risk and the assessment of monetary value. However, there is a lack of empirical evidence available that addresses how investors’ emotions affect commodity market returns. The purpose of this paper is to investigate whether media-based emotions can be used to predict future commodity returns.Design/methodology/approachThe authors examine the short-term predictive power of media-based emotion indices on the following five days’ commodity returns. The research adopts a proprietary data set of commodity-specific market emotions, which is computed based on a comprehensive textual analysis of sources from newswires, internet news sources and social media. Time series econometrics models (threshold generalized autoregressive conditional heteroskedasticity and vector autoregressive) are employed to analyze 14 years (January 1998-December 2011) of daily observations of the CRB commodity market index, crude oil and gold returns, and the market-level sentiments and emotions (optimism, fear and joy).FindingsThe empirical results suggest that the commodity-specific emotions (optimism, fear and joy) have significant influence on individual commodity returns, but not on commodity market index returns. Additionally, the research findings support the short-term predictability of the commodity-specific emotions on the following five days’ individual commodity returns. Compared to the previous studies of news sentiment on commodity returns (Borovkova, 2011; Borovkova and Mahakena, 2015; Smales, 2014), this research provides further evidence of the effects of news and social media-based emotions (optimism, fear and joy) in the commodity market. Additionally, this work proposes that market emotion incorporates both a sentimental effect and appraisal effect on commodity returns. Empirical results are shown to support both the sentimental effect and appraisal effect when market sentiment is controlled in crude oil and gold spot markets.Originality/valueThis paper adopts the valence-arousal approach and cognitive appraisal approach to explain financial anomalies caused by investors’ emotions. Additionally, this is the first paper to explore the predictive power of investors’ emotions (optimism, fear and joy) on commodity returns.

Gain Control in Predictive Smooth Pursuit Eye Movements: Evidence for an Acceleration-Based Predictive Mechanism.

The smooth pursuit eye movement system incorporates various control features enabling adaptation to specific tracking situations. In this work, we analyzed the interplay between two of these mechanisms: gain control and predictive pursuit. We tested human responses to high-frequency perturbations during step-ramp pursuit, as well as the pursuit of a periodically moving target. For the latter task, we found a nonlinear interaction between perturbation response and carrier acceleration. Responses to perturbations where the initial perturbation acceleration was contradirectional to carrier acceleration increased with carrier velocity, in a manner similar to that observed during step-ramp pursuit. In contrast, responses to perturbations with ipsidirectional initial perturbation and carrier acceleration were large for all carrier velocities. Modeling the pursuit system suggests that gain control and short-term prediction are separable elements. The observed effect may be explained by combining the standard gain control mechanism with a derivative-based short-term predictive mechanism. The nonlinear interaction between perturbation and carrier acceleration can be reproduced by assuming a signal saturation, which is acting on the derivative of the target velocity signal. Our results therefore argue for the existence of an internal estimate of target acceleration as a basis for a simple yet efficient short-term predictive mechanism.

Short-Term Predictability and Price Impact

We examine short-horizon return predictability using a novel proprietary dataset of institutional traders with known identities. We estimate investor-specific short-term trading skill and find that there is pronounced heterogeneity in predicting short-term returns among institutional investors. Incorporating short-term predictive ability, our model explains much higher fraction of variation in asset returns. Ignoring the heterogeneity in short-term trading skill has major implications in modeling price impact. We analyze the differences between trading characteristics of skilled and unskilled investors. A simple trading strategy exploiting our skill estimates yields statistically significant abnormal returns supporting the skill-based interpretation.

A prediction model based on neural networks for the energy consumption of a bioclimatic building

Energy in buildings is a topic that is being widely studied due to its high impact on global energy demand. This problem involves the performance of an adequate management of the energy demand, combining both convectional and renewable sources. To this end, the use of control strategies is an important tool. These control strategies can take advantage of knowledge of variables that act as disturbances in the closed loop scheme. Thus, it is of great importance the development of predictions of such variables. The main objective of this paper is to develop and assess a short-term predictive neural network model of the electricity demand for the CIESOL bioclimatic building, located in the southeast of Spain. The performed experiments show a quick prediction with acceptable final results for real data with a short-term prediction horizon equal to 60min and with a mean error of 11.48%. One-step ahead predictions and dynamic modeling simulations have also been evaluated.

Chaos and Organizational Emergence: Towards Short Term Predictive Modeling to Navigate a Way Out of Chaos

Organizational emergence from chaotic events has often been described as unpredictable and random, at least when observed over the long term. Through a literature review this paper paves the path for the possible development of a forecasting model for making short term predictions during chaotic events so as to assist business practitioners in navigating their way out of chaos, thereby facilitating organizational emergence. As such, the paper builds on chaos and complexity theory, as its foundational theory, and identifies the most important factors and mechanisms cited in current literature that influence organizational emergence so as to potentially incorporate the factors in the future development of a short term forecasting model.

OP14.12: Independent and combined contribution of cardiovascular Doppler parameters to short‐term prediction of mortality in severe IUGR

Objectives: To evaluate the independent and combined contribution to short-term predictive value for mortality in severe IUGR of clinically used and recently described cardiovascular Doppler parameters. Methods: Umbilical artery, middle cerebral artery, ductus venosus (DV), aortic isthmus flow index and modified-myocardial performance index (Mod-MPI) were measured in a cohort of 97 preterm IUGR fetuses. Logistic regression was performed to select those variables that were independently associated to perinatal mortality, and an algorithm to estimate probabilities of death was constructed including the best combination of parameters. Results: A combination of gestational age (below or above 28 weeks), DV atrial flow (positive or absent-reverse) and ModMPI (normal or above 95th percentile) had the highest predictive value for perinatal mortality in IUGR cases. An algorithm including these 3 parameters had a better predictive accuracy than any single parameter. The risk for death below and above 28 weeks respectively was: normal DV and Mod-MPI, 18% and 0.1%; either abnormal, 70–73% and 6–7%; both abnormal, 97% and 45%. Conclusions: The use of composite cardiovascular Doppler scores may help to refine considerably the short-term prediction of perinatal mortality at different gestational ages in severe IUGR fetuses.