Analysis and Forecasting of Blockchain-based Cryptocurrencies and Performance Evaluation of TBATS, NNAR and ARIMA

Sadia, Iqra; Mahmood, Atif; Kiah, Miss Laiha Mat; Azzuhri, Saaidal Razalli

doi:10.1109/iicaiet55139.2022.9936798

Cited by 3 publications

(1 citation statement)

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“…The ARIMA procedure's broad transfer function model. When additional time series are included as input variables for an ARIMA model, it is frequently referred to as an autoregressive integrated moving average with explanatory variable (ARIMAX) model [14], [15]. The ARIMAX model is referred to as dynamic regression by Jay et al [16].…”

Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%

Performance analysis of bitcoin forecasting using deep learning techniques

Tripathy

Hota

Mishra

2023

IJEECS

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The most popular cryptocurrency used worldwide is bitcoin. Many everyday folks and investors are now investing in bitcoin. However, it becomes quite difficult to evaluate or foresee the price of bitcoin. The price of bitcoin is extremely difficult to forecast due to its swings. By this point, machine learning has developed a number of models to examine the price behaviour of bitcoin using time series data. The digital money, a different type of payment developed utilising encryption methods, is difficult to forecast. By utilising encryption technology, cryptocurrencies may act as both a medium of exchange and a virtual accounting system. To estimate the values of a future time sequence, this work introduces a deep learning-based technique for time series forecasting that treats the current data as time series and extracts the key traits of the past. To overcome the shortcomings of conventional production forecasting, three algorithms-auto-regressive integrated moving averages (ARIMA), long-short-term memory (LSTM) network, and FB-prophet-were investigated and contrasted. We compared the models using historical bitcoin data of past eight years, from 2012 to 2020. The “FB-prophet” model, which is significant, catches variation that might draw attention and avert possible problems.

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Section: Autoregressive Integrated Moving Averagementioning

confidence: 99%