FORECASTING BITCOIN PRICES WITH TIME SERIES ANALYSIS IN PYTHON AND EXCEL - Joshi, JayKrishna; Gharat, Anish; Chheda, Snehee; Sharma, R.P. (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Over the past few years the interest in trading with a decentralized or virtual form of currency has significantly increased. This has led to the rise of the cryptocurrency market in the early 2000’s. Bitcoin, the pioneer in the field, has managed to dominate this volatile and cutthroat market till this day. Cryptocurrency is based on multiple technological frameworks and success stories of high returns in a short time frame have garnered the interest of young investors as well. Similar to the traditional stock market, multiple machine learning, Artificial Intelligence, Time Series Analysis models have come up to help investors, understand trends, patterns, and derive a deeper understanding of the asset as well as the market they are investing in. Our research aims to deal with this problem using time series methods such as Auto-Regressive (AR), Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA) models. In our analysis, we have implemented the models using both Excel method and Python. Our metric of evaluation is Mean Absolute Percentage Error (MAPE). In our work, data was taken from a website called ‘Yahoo finance’ [1] for Bitcoin cryptocurrency for a five-year time period i.e. from 1st June, 2017 to 31st May, 2024. Our Python methodology has resulted in a MAPE of 0.16 for AR model, 0.14 for MA model and 0.11 for ARIMA model. The same has been verified using Excel and the score has been validated.