  This paper is based on a machine learning project at the Norwegian University
of Science and Technology, fall 2020. The project was initiated with a
literature review on the latest developments within time-series forecasting
methods in the scientific community over the past five years. The paper
summarizes the essential aspects of this research. Furthermore, in this paper,
we introduce an LSTM cell's architecture, and explain how different components
go together to alter the cell's memory and predict the output. Also, the paper
provides the necessary formulas and foundations to calculate a forward
iteration through an LSTM. Then, the paper refers to some practical
applications and research that emphasize the strength and weaknesses of LSTMs,
shown within the time-series domain and the natural language processing (NLP)
domain. Finally, alternative statistical methods for time series predictions
are highlighted, where the paper outline ARIMA and exponential smoothing.
Nevertheless, as LSTMs can be viewed as a complex architecture, the paper
assumes that the reader has some knowledge of essential machine learning
aspects, such as the multi-layer perceptron, activation functions, overfitting,
backpropagation, bias, over- and underfitting, and more.
