International Journal of Advanced Engineering Research and Science (IJAERS) [Vol-6, Issue-9, Sept- 2019] https://dx.doi.org/10.22161/ijaers.69.18 ISSN: 2349-6495(P) | 2456-1908(O) www.ijaers.com Page | 161 Forecasting bitcoin pricing with hybrid models: A review of the literature Olvera-Juarez D. 1 , Huerta-Manzanilla E. 2 1 Facultad de Ingeniería, Universidad Autónoma de Querétaro, MX. Email: denzelolvera@gmail.com 2 Facultad de Ingeniería, Universidad Autónoma de Querétaro, MX. Email:eric.huerta@uaq.mx Abstract—The electronic transition has been gaining a large groundin recent decades due to the use of crypto currencies. One of the most popular is Bitcoin. It is open source, the transactions and the issuance of bitcoins occur collectively through the network.The analysis of the behavior of Bitcoin becomes a relevance to the prediction Price and achieve successful investments in it.This review is conducted for the analysis and comparison of the of the different prediction methods focused on the bitcoin price. Anemphasis is placed on those who have a structure as the basis of the ARIMA model, then adding to the hybrid methods, which use neural networks to complete the method. Keywords—Bitcoin, cryptocurrency, moving average, autoregressive, price prediction. Objective. A review of the literature including ARIMA techniques applied in bitcoin forecasting is presented. A summary of metanalysis findings was prepared and a research agenda of potential further work is defined. I. INTRODUCTION In the last decades, the globalization and the technology brought great changes in several sectors, such as the economy and administration. One of those changes is electronic money, a new payment method. The cryptocurrency is an electronic currency, due it uses cryptographic tests to control the additional units and verify the transfer of assets. (Nakamoto, 2008). The cryptocurrencies are a peer to peer version of commerce. The main advantage of these transactions is that payments can be sent from one user to another. Due to the financial crisis of 2008, interest in cryptocurrencies returned. Cryptocurrencies may have the ability to face several problems relevantforfiat currency system, right at the beginning of the global financial crisis [1]. In fact, Bitcoin was born as a decentralized network and as a digital currency. Internet users split it by using a B to refer to the network. Bitcoin technology uses cryptographic tests in its software to process transactions and verify the legitimacy of bitcoins and distributes the processing work among the network [2]. This was developed to avoid using trusted third parties, such as bank and cards. At first Bitcoin operations, it was possible to make payments in the internet without restraint, and without the costs of central authorities. This allows the behavior of bitcoin as an analogy of assets transference, retaining its value by itself. At the same time, the bitcoin achieves the economic definition of money: it is a mean of Exchange, unit of account and storage of value. [1]. II. PREDICTION TECHNIQUES 2.1 Autoregressive Integrated Moving Average (ARIMA). The autoregressive integrated mobile average (ARIMA) is the most common and widely used time series model. Due to its statics properties this model is very important. [3]. This tool can develop several exponential smoothing models and could work in some types of time series, without losing the original characteristics or the time series. The ARIMA model approach outperforms pure autoregressive series (AR), pure moving averages (MA) and combined AR and MA (ARMA) series models. An important lack of scope of these individual techniques is that they presuppose that the time series are linear (Zhang, 2003). Using the linear model in the real world, complex processes cannot be represented and have successful results. The ARIMA model has an advantage, this model has individual components that describe trend, error and seasonality separately (p, d, q). That is why nonlinear models can be represented.