A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting

In practice, time series forecasting involves the creation of models that generalize data from past values and produce future predictions. Moreover, regarding financial time series forecasting, it can be assumed that the procedure involves phenomena partly shaped by the social environment. Thus, the present work is concerned with the study of the use of sentiment analysis methods in data extracted from social networks and their utilization in multivariate prediction architectures that involve financial data. Through an extensive experimental process, 22 different input setups using such extracted information were tested, over a total of 16 different datasets, under the schemes of 27 different algorithms. The comparisons were structured under two case studies. The first concerns possible improvements in the performance of the forecasts in light of the use of sentiment analysis systems in time series forecasting. The second, having as a framework all the possible versions of the above configuration, concerns the selection of the methods that perform best. The results, as presented by various illustrations, indicate, on the one hand, the conditional improvement of predictability after the use of specific sentiment setups in long-term forecasts and, on the other, a universal predominance of long short-term memory architectures.

Study On Multiscale-Multivariate Attribution Analysis and Prediction of Urban Rainstorm Flood

In order to explore the impact of the changing environment on urban rainstorm flood, and reveal the relationship between flood volume and its influencing factors at the micro level, the rainfall and flood volume are decomposed by the wavelet analysis method to perform the multiscale attribution analysis. Then the multiscale-multivariate prediction model of urban rainstorm flood is constructed in the Jialu River Basin in Zhengzhou city of China. The results show that the main influencing factors of flood volume are rainfall and underlying surface, where the latter causes the mutation of flood volume in 1994 and 2005. At the micro level, there is a constant linear relationship between rainfall and flood volume in d1, d2 and d3, while the impact of underlying surface on flood volume is mainly reflected in a3. The multiscale-multivariate prediction model has a good simulation effect on the flood volume of the first 45 rainstorm floods, NSE, R2 and Re are 0.966, 0.964 and 10.80%, respectively. Moreover, the model also has a good prediction effect, and the relative errors between the predicted and observed flood volume of 46th~50th rainstorm floods are all less than 20%.

Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions

This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable.

Developing a multivariate prediction model of antibody features associated with protection of malaria-infected pregnant women from placental malaria

Background:Plasmodium falciparum causes placental malaria, which results in adverse outcomes for mother and child. P. falciparum-infected erythrocytes that express the parasite protein VAR2CSA on their surface can bind to placental chondroitin sulfate A. It has been hypothesized that naturally acquired antibodies towards VAR2CSA protect against placental infection, but it has proven difficult to identify robust antibody correlates of protection from disease. The objective of this study was to develop a prediction model using antibody features that could identify women protected from placental malaria.Methods:We used a systems serology approach with elastic net-regularized logistic regression, partial least squares discriminant analysis, and a case-control study design to identify naturally acquired antibody features mid-pregnancy that were associated with protection from placental malaria at delivery in a cohort of 77 pregnant women from Madang, Papua New Guinea.Results:The machine learning techniques selected 6 out of 169 measured antibody features towards VAR2CSA that could predict (with 86% accuracy) whether a woman would subsequently have active placental malaria infection at delivery. Selected features included previously described associations with inhibition of placental binding and/or opsonic phagocytosis of infected erythrocytes, and network analysis indicated that there are not one but multiple pathways to protection from placental malaria.Conclusions:We have identified candidate antibody features that could accurately identify malaria-infected women as protected from placental infection. It is likely that there are multiple pathways to protection against placental malaria.Funding:This study was supported by the National Health and Medical Research Council (Nos. APP1143946, GNT1145303, APP1092789, APP1140509, and APP1104975).

COVID-Related Concerns, the Need for Help, and Perceived Microaggression among Young Ultra-Orthodox Jewish Respondents in Israel

Minority groups are especially vulnerable to the negative psychological and economic consequences of the COVID-19 pandemic. This study focused on one prominent minority group in Israel: ultra-Orthodox Jews. It examined the rate of exposure to COVID-19, adherence to COVID-19 mitigation guidelines, difficulties with adherence to COVID-19 guidelines, COVID-related concerns, financial hardships, the need for help, and microaggression during the first wave of the pandemic (April–May 2020). It then examined multivariate prediction of COVID-related concerns, the need for help, and microaggression. The sample comprised 252 respondents, with 67% female and a mean age of 32.85 (SD = 10.63). Results showed that 78.8% of the participants knew at least one person who had tested positive for COVID-19, and 31.4% knew at least one person who had passed away from COVID-19. Only 59.7% of the participants reported high adherence to social distancing guidelines. Perceived microaggression was predicted by the difficulties with adherence to COVID-19 guidelines, the level of stress associated with exposure to the media, and financial hardships. The study’s implications point to the centrality of perceived microaggression and the necessity of adopting culturally sensitive approaches to engage minorities in public efforts to fight the spread of viruses.

2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.