Bitcoin data is very different from temperature data. Temperatures have more or less the same value for the same month of each year. This indicates that the distribution of temperatures does not change over time. Time series that exhibit this behavior are called stationary. This allows for relatively easy modeling with time series analysis tools, such as auto regressive (AR), moving average (MA), and auto regressive integrated moving average (ARIMA) models. Financial data is usually non-stationary, as seen in the daily Bitcoin close data, depicted in figure. This means that the data does not exhibit the same behavior throughout its entire history, but instead its behavior varies.
Financial data usually provides open ...
