Abstract
Analyzing time-series data have gained significant attention in modern research works. Its significance lies in different applications such as weather forecasting, economic forecasting, sales forecasting, etc. This project aims to explore the efficiency of various approaches in predicting time-series cryptocurrency prices data. This work is an experimental study on the potential of different approaches. These approaches included Auto-Regressive (AR), Moving Average (MA), Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Independently RNN (IndRNN), and Fine-tuned IndRNN. The dataset was a quantitative secondary historical time-series data of Ethereum cryptocurrency prices collected from a reliable bulletin. The suggested approaches were implemented and tested on the Ethereum Cryptocurrency historical prices where Ethereum is the second-largest cryptocurrency by market share at our time. IndRNN and its fine-tuned version were proved to yield higher prediction potential than the other presently utilized methods with MSE of 239 and 213, respectively. Also, deep learning models, outperformed the linear models, in terms of accuracy of prediction. The tested approaches can be utilized as a standard in predicting cryptocurrency data with acceptable accuracy. These models can be used by investors to help them make good decisions in buying or selling cryptocurrency stocks.
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Alsharef, A., Sonia, Arora, M., Aggarwal, K. (2022). Predicting Time-Series Data Using Linear and Deep Learning Models—An Experimental Study. In: Sharma, S., Peng, SL., Agrawal, J., Shukla, R.K., Le, DN. (eds) Data, Engineering and Applications. Lecture Notes in Electrical Engineering, vol 907. Springer, Singapore. https://doi.org/10.1007/978-981-19-4687-5_39
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