ABSTRACT
Copper and aluminium prices have long been influenced mainly by non-renewable resources and the industry's widespread use of copper and aluminium for their desired properties. Metal commodities are irreplaceable for the industry of developed countries, and their shortage in the covid times also increases the price and consequently the price of products made from them. As copper ore stocks continue to decline, suitable substitutes should be sought. The paper discusses the potential of copper substitution by aluminium and subsequently the development of prices and production of copper and aluminium, including a prediction about the future development. Research data were obtained from Market. business insider (2021) and Investing.com (2022) converted to time series. The price is shown in US dollars per tonne and the production value in millions of tonnes. Development data were processed using artificial intelligence and recurrent neural networks, including the Long Short Term Memory layer. Neural networks, as such, have great potential to predict these types of time series. The annual copper and aluminium production data were processed using a regression function. Neural networks could not be used due to the smaller data range. The results show that the 1NN30L neural network with an LSTM layer and considered a 30-day delay is the most suitable network for forecasting future copper prices, and the 3NN30L neural network with an LSTM layer and considered a 30-day delay is the most suitable network for forecasting future aluminium prices. The forecast has confirmed that the price of copper will fall at the end of 2021, and the trend will be constant in the next planned period. Aluminium will also fall sharply at the end of 2021;at the beginning of 2022, the price level is predicted to rise to that of 30 October 2021, and thereinafter the trend will be almost constant. Research has confirmed that copper and aluminium may be imperfect substitutes in some respect, but they can generally be considered complementary. Copper mining has stabilised in recent years, but aluminium production has increased significantly in the last decade, and it can be expected to grow in the near future. © 2022 by the authors.
ABSTRACT
The increasingly meagre copper ore resources constitute one of the decisive factors influencing the price of this commodity. The demand for copper has been showing an accelerating trend since the Covid pandemic broke out. It is thereby imperative to estimate the future price movement of this material. The article focuses on a daily prediction of the forthcoming change in prices of copper on the commodity market. The research data were gathered from day-to-day closing historical prices of copper from commodity stock COMEX converted to a time series. The price is expressed in US Dollars per pound. The data were processed using artificial intelligence, recurrent neural networks, including the Long Short Term Memory layer. Neural networks have a great potential to predict this type of time series. The results show that the volatility in copper price during the monitored period was low or close to zero. We may thereby argue that neural networks foresee the first three months more accurately than the rest of the examined period. Neural structures anticipate copper prices from 4.5 to 4.6 USD to the end of the period in question. Low volatility that would last longer than one year would cut down speculators' profits to a minimum (lower risk). On the other hand, this situation would bring about balance which the purchasing companies avidly seek for. However, the presented article is solely confined to a limited number of variables to work with, disregarding other decisive criteria. Although the very high performance of the experimental prediction model, there is always space for improvement - e.g. effectively combining traditional methods with advanced techniques of artificial intelligence.
ABSTRACT
We draw on a substantial body of theoretical and empirical research on hesitancy towards receiving a COVID-19 vaccine in terms of concerns about side effects and safety, mistrust in government and health authorities, and perceived susceptibility and severity of the virus, and to explore this, we inspected, used, and replicated survey data from The British Academy, CEAL, de Beaumont, Jones et al. (2021), KFF, Medscape, MSDH/OPHHE, The Royal Society, Tegan and Sara Foun-dation, and W2O, performing analyses and making estimates regarding COVID-19 vaccine perceptions, attitudes and behaviors. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. © 2021, Addleton Academic Publishers. All rights reserved.