Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice

Four Decades of Surface Temperature, Precipitation, and Wind Speed Trends over Lakes of Greece

Climate change is known to affect world’s lakes in many ways. Lake warming is perhaps the most prominent impact of climate change but there is evidence that changes of precipitation and wind speed over the surface of the lakes may also have a significant effect on key limnological processes. With this study we explored the interannual trends of surface temperature, precipitation, and wind speed over 18 lakes of Greece using ERA5-Land data spanning over a period of almost four decades. We used generalized additive models (GAMs) to conduct time-series analysis in order to identify significant trends of change. Our results showed that surface temperature has significantly increased in all lakes with an average rate of change for annual temperature of 0.43 °C decade−1. With regard to precipitation, we identified significant trends for most lakes and particularly we found that precipitation decreased during the first two decades (1981–2000), but since 2000 it increased notably. Finally, wind speed did not show any significant change over the examined period with the exception for one lake. In summary, our work highlights the major climatic changes that have occurred in several freshwater bodies of Greece. Thus, it improves our understanding on how climate change may have impacted the ecology of these important ecosystems and may aid us to identify systems that are more vulnerable to future changes

Unravelling Precipitation Trends in Greece since 1950s Using ERA5 Climate Reanalysis Data

Precipitation is one of the most variable climatic parameters, as it is determined by many physical processes. The spatiotemporal characteristics of precipitation have been significantly affected by climate change during the past decades. Analysis of precipitation trends is challenging, especially in regions such as Greece, which is characterized by complex topography and includes several ungauged areas. With this study, we aim to shed new light on the climatic characteristics and inter-annual trends of precipitation over Greece. For this purpose, we used ERA5 monthly precipitation data from 1950 to 2020 to estimate annual Theil–Sen trends and Mann–Kendall significance over Greece and surrounding areas. Additionally, in order to analyze and model the nonlinear relationships of monthly precipitation time series, we used generalized additive models (GAMs). The results indicated significant declining inter-annual trends of areal precipitation over the study area. Declining trends were more pronounced in winter over western and eastern Greece, but trends in spring, summer and autumn were mostly not significant. GAMs showcased that the trends were generally characterized by nonlinearity and precipitation over the study area presented high inter-decadal variability. Combining the results, we concluded that precipitation did not linearly change during the past 7 decades, but it first increased from the 1950s to the late 1960s, consequently decreased until the early 1990s and, afterwards, presented an increase until 2020 with a smaller rate than the 1950–1960s

Unravelling Precipitation Trends in Greece since 1950s Using ERA5 Climate Reanalysis Data

Precipitation is one of the most variable climatic parameters, as it is determined by many physical processes. The spatiotemporal characteristics of precipitation have been significantly affected by climate change during the past decades. Analysis of precipitation trends is challenging, especially in regions such as Greece, which is characterized by complex topography and includes several ungauged areas. With this study, we aim to shed new light on the climatic characteristics and inter-annual trends of precipitation over Greece. For this purpose, we used ERA5 monthly precipitation data from 1950 to 2020 to estimate annual Theil–Sen trends and Mann–Kendall significance over Greece and surrounding areas. Additionally, in order to analyze and model the nonlinear relationships of monthly precipitation time series, we used generalized additive models (GAMs). The results indicated significant declining inter-annual trends of areal precipitation over the study area. Declining trends were more pronounced in winter over western and eastern Greece, but trends in spring, summer and autumn were mostly not significant. GAMs showcased that the trends were generally characterized by nonlinearity and precipitation over the study area presented high inter-decadal variability. Combining the results, we concluded that precipitation did not linearly change during the past 7 decades, but it first increased from the 1950s to the late 1960s, consequently decreased until the early 1990s and, afterwards, presented an increase until 2020 with a smaller rate than the 1950–1960s

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

Background A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. Methods Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. Results About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. Conclusions People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people