Gold has become more popular as well as very useful commodity in terms of investment. Gold has been used as a national reserve for many years, and that makes it very crucial in the economics of any country. Most of the investors running to gold as a safe area from uncertainty and political chaos. Determining of the price movement of gold helps the investors in focus in their investments, government to make correct decision about economy since Gold price is a key element is world economy. For the purpose of predicting the price of gold, this article research uses ARIMA and SVM model in prediction. The study uses the daily data from world Gold Council from 1979 to 2019 in analysis. The data up to 2014 are used for the training of the models and the rest are used validation. The study results show that the SVM is better one compares to ARIMA using the performance measurement tools of RMSE and MAPE by having RMSE of 0.028 and MAPE of 2.5 for the SVM and 36.18 and 2897 for ARIMA respectively. The results suggest SVM to be used in prediction of any commodity price due to his high accuracy.

After crude oil, natural gas has become one of the most important energy sources in the world. For a long time, Tanzania has been exploring for natural gas. The first gas discovering was in 1974 in Songo Songo island southern part of Tanzania and the production started in 2004. It is always believed that gas consumption leads to the growth and development in the economics of that area. The uses of gas in homes, industries increase the living standard of the people. This study concentrates on finding the relationship between natural gas consumption and economic growth in Tanzania by using autoregressive distributed lag model, with the data from TPDC, and World Bank from 1995 to 2018. Economic growth is mainly determined by GDP, FDI, increase of population in the urban and inflation rate. The result of this study indicates that there is no long-run relationship between gas consumption and economic growth. On top of that, causality is only found in Gas consumption to FDI.

Crude oil plays a big role in determining the world economy today. The increase in the oil price leads to an increase in inflation and hence reduces economic growth. More to that from crude oil, different products reduce. Therefore, a change in oil prices will directly affect these products.Because of this, it is very important to determine the future price of crude oil for better economy budgeting and future planning. Knowing the future price of oil is very challenging. Investors, business people, and the government need accurate prediction models for their decision-making.The main challenge of predicting the price of crude oil is the instability of the price of crude oil.In this paper, the study will use the deep learning techniques to capture the behavior of the crude oil price with a comparison with the other three techniques. The study will use Long Short Term Memory (LSTM) with a comparison with the Moving average (MA), linear regression (LR) and Autoregressive integrated moving average (ARIMA). Using the data from West Texas Index Intermediate (WTI), and measurement performance RMSE and R-Square, this research has proved