Scanning the horizon : forecasting and trading on forward freight agreements using long short-term memory neural networks and AIS-derived features

The purpose of this study has been to predict Forward Freight Agreement (FFA) prices using machine learning techniques, investigate the additional forecasting power of Automatic Identification System (AIS) derived features, and to evaluate the profitability of applying forecasted directional movements to trading strategies. A Long-Short-Term Memory (LSTM) neural network is used to predict price movements for the two closest quarterly, and the closest calendar year Capesize 5 Time Charter (5TC) FFAs. We have derived features from AIS data to generate proxies for supply, demand and geographical distribution for a subset of Capesize vessels. Additionally, we have included commodity prices and macroeconomic variables. The forecasting horizon investigated has been one week, two weeks, and one month ahead. To benchmark the LSTM model, we have included Vector Autoregressive (VAR) models, Autoregressive Integrated Moving Average (ARIMA) models and a Random Walk. The VAR models were found to be superior at forecasting FFA prices, and the results showed that the LSTM neural network and VAR show potential for predicting directional movements of prices. The results further indicate that AIS data holds predictive capabilities regarding directional movements of prices. Lastly, the trading results give implications of increased profitability compared to buy-and-hold and trend-following benchmarks, by utilizing the trading signals from the models.nhhma

Scanning the horizon : forecasting and trading on forward freight agreements using long short-term memory neural networks and AIS-derived features

The purpose of this study has been to predict Forward Freight Agreement (FFA) prices using machine learning techniques, investigate the additional forecasting power of Automatic Identification System (AIS) derived features, and to evaluate the profitability of applying forecasted directional movements to trading strategies. A Long-Short-Term Memory (LSTM) neural network is used to predict price movements for the two closest quarterly, and the closest calendar year Capesize 5 Time Charter (5TC) FFAs. We have derived features from AIS data to generate proxies for supply, demand and geographical distribution for a subset of Capesize vessels. Additionally, we have included commodity prices and macroeconomic variables. The forecasting horizon investigated has been one week, two weeks, and one month ahead. To benchmark the LSTM model, we have included Vector Autoregressive (VAR) models, Autoregressive Integrated Moving Average (ARIMA) models and a Random Walk. The VAR models were found to be superior at forecasting FFA prices, and the results showed that the LSTM neural network and VAR show potential for predicting directional movements of prices. The results further indicate that AIS data holds predictive capabilities regarding directional movements of prices. Lastly, the trading results give implications of increased profitability compared to buy-and-hold and trend-following benchmarks, by utilizing the trading signals from the models

Hvilke faktorer bidrar til SARS-CoV-2 sin spredningsevne?

I denne oppgaven har det blitt forsøkt å samle informasjon om faktorer som bidrar til SARS-CoV-2 sin spredningsevne. Som metodevalg er litteratursøk med bruk av over 20 vitenskapelige artikler benyttet. SARS-CoV-2 tilhører gruppen IV i Baltimore-klassifikasjonen over virus. Det antydes at viruset stammer originalt fra kinesisk flaggermus, men det er sannsynligvis intermediære smittekilder som førte smitten til mennesker. Såpe ser ut til å være en svært effektiv måte å inaktivere SARS-CoV-2 på hudoverflater. Det samme er desinfeksjonsmiddel, men det har ulemper som at man trenger større mengde desinfeksjonsmiddel enn såpe for å dekke de samme områdene. Inkubasjonstiden til SARS-Cov-2 for å bli symptomatisk sykdom er mellom 2 - 14 dager, med gjennomsnittet mellom 5 - 6 dager. Dette stemmer godt overens med beskjeder fra offentlige helsetjenester som FHI og WHO. SARS-CoV-2 forårsaker sykdommen COVID-19. I noen sykdomsforløp vil personer være asymptomatiske, men fortsatt kunne spre viruset til andre. Det kan spres ved direkte- og indirekte kontakt eller via aerosoler. SARS-CoV-2 ser ut til å binde seg til ACE2-reseptorer. I tillegg er det sannsynlig at viruset er avhengig av TMPRSS2 for bearbeiding av S-protein for å infisere mennesker. Det ser ut til at blodtrykksmedisin ikke fører til lettere infeksjon med SARS-CoV-2 hos eldre, da det ikke er forskning som tilsier dette. Virusets spredning er sannsynligvis fremmet pga. høy menneskelig mobilitet i sammenheng med vårfestivalen i Kina, mangel på effektiv håndvask, potensielt svært høy affinitet til ACE2-reseptorer, og uvitende, udokumenterte smittede som sprer viruset

Degrading mountain permafrost in southern Norway: spatial and temporal variability of mean ground temperatures, 1999–2009

A ten-year record (1999–2009) of annual mean ground surface temperatures (MGSTs) and mean ground temperatures (MGTs) was analysed for 16 monitoring sites in Jotunheimen and on Dovrefjell, southern Norway. Warming has occurred at sites with cold permafrost, marginal permafrost and deep seasonal frost. Ongoing permafrost degradation is suggested both by direct temperature monitoring and indirect geophysical surveys. An increase in MGT at 6.6–9.0-m depth was observed for most sites, ranging from ~0.015 to ~ 0.095°C a-1. The greatest rate of temperature increase was for sites having MGTs slightly above 0°C. The lowest rate of increase was for marginal permafrost sites that are affected by latent heat exchange close to 0°C. Increased snow depths and an increase in winter air temperatures appear to be the most important factors controlling warming observed over the ten-year period. Geophysical surveys performed in 1999 to delineate the altitudinal limit of mountain permafrost were repeated in 2009 and 2010 and indicated the degradation of some permafrost over the intervening decade

(Tables 1, 2) Permafrost borehole characteristics in the Nordic Area during the IPY 2007-2009

This paper provides a snapshot of the permafrost thermal state in the Nordic area obtained during the International Polar Year (IPY) 2007-2009. Several intensive research campaigns were undertaken within a variety of projects in the Nordic countries to obtain this snapshot. We demonstrate for Scandinavia that both lowland permafrost in palsas and peat plateaus, and large areas of permafrost in the mountains are at temperatures close to 0°C, which makes them sensitive to climatic changes. In Svalbard and northeast Greenland, and also in the highest parts of the mountains in the rest of the Nordic area, the permafrost is somewhat colder, but still only a few degrees below the freezing point. The observations presented from the network of boreholes, more than half of which were established during the IPY, provide an important baseline to assess how future predicted climatic changes may affect the permafrost thermal state in the Nordic area. Time series of active-layer thickness and permafrost temperature conditions in the Nordic area, which are generally only 10 years in length, show generally increasing active-layer depths and rising permafrost temperatures