Forecasting Ethereum STORJ Token Prices: Comparative Analyses of Applied Bitcoin Models The research on forecasting Ethereum STORJ token has not been widely studied compared to forecasting Bitcoin. The objective of this paper is threefold: apply existing Bitcoin price forecasting models to the Ethereum STORJ token price; evaluate the dynamics of the model predictive utility across three time horizons (h5 days, h20 days and h50 days); and determine if Ethereum STORJ token clustering coefficients impact the effectiveness of the forecasting model. We choose Bitcoin forecasting models of: ARIMA, ARMAGARCH, VAR, alphaSutte Indicator and NNAR. Model effectiveness is analyzed using RMSE, MAE and MAPE. We find that VAR outperforms all models in the short and midterm horizons (h5 and h20 days) and NNAR outperforms all models in the longterm horizon (h50 days). Nonlinearity, the intrinsic value that Neural Network has, may strongly effect the forecast accuracy result. When adding the clustering coefficient to ARIMA, we find that the variable is significant but only marginally improves the forecasting of the Ethereum STORJ token. The VAR model, which includes the clustering coefficient is shown to better forecast Ethereum STORJ prices in the short and midterm forecasting horizons. The NNAR model is a better model for the longterm forecasting horizon.

Comparative study of Bitcoin price prediction using WaveNets, Recurrent Neural Networks and other Machine Learning Methods Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with their many variations that can effectively forecast. However, with the recent advancement in the computational capacity of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms have been developed to forecast time series data. This article compares different methodologies such as ARIMA, Random Forest (RF), Support Vector Machine (SVM), Long ShortTerm Memory (LSTM) and WaveNets for estimating the future price of Bitcoin.