Estimating Blockchain Using Time-Series Forecasting ARIMA

Bhatnagar, Vaibhav; Batra, Bhoomika

doi:10.1007/978-981-19-1122-4_50

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2023

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“…Traditionally, statistical techniques such as Auto-Regressive Integrated Moving Average (ARIMA) [9][10][11] and Exponential Smoothing (ETS) [12,13] dominated the field of forecasting. However, their performance in realworld applications is limited due to their restrictive assumptions and parametric nature [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet

Noviandy,

Maulana,

Idroes

et al. 2023

Ekon. J. Econ.

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This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.

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