Description
TL;DR: A compact survey of period forecasting methods, with emphasis on combining statistical time-series models and neural networks.
About This Document
Denzel Olvera-Juarez and Eric Huerta-Manzanilla review Bitcoin price-prediction studies built around ARIMA and hybrid machine-learning structures. The paper compares selected methods and motivations rather than publishing a live forecast, trading system, or independently reproduced benchmark.
Why Bitcoin People May Care
Historical fit does not establish future predictive power, especially in a volatile market with changing regimes and data leakage risks. This short review is useful for tracing the modeling approaches discussed in 2019, but it is not investment advice, a validated strategy, or a promise that any hybrid model can forecast Bitcoin reliably.
What You Receive
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Document Details
- Title: Forecasting Bitcoin Prices with Hybrid Models — Literature Review
- Author / organization: Denzel Olvera-Juarez and Eric Huerta-Manzanilla
- Year: 2019
- Language: English
- Document type: Bitcoin forecasting literature review
- Pages: 4
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