2024-05-10 Bitcoin News
2024-05-09
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Overall Trend Analysis
Analysing the given data, the trend in the bitcoin exchange rate seems quite fluctuating. It goes through a series of rises and falls. With the first data point standing at 84415.70446, the final rate listed is 86092.43814. However, the highest point in the data is 85774.3351, showing a large rise in between. The data doesn't clearly provide an overall increasing or decreasing trend, but to conclude, the rate at the final timestamp is higher than at the initial timestamp.
Seasonality or Recurring Patterns
Considering the seasonality or recurring patterns, it's challenging to assert without a larger dataset. Given the data is for 24 hours, there are no clear indications of seasonality or recurring daily patterns. The change of exchange rates is not regular and shows a complex random walk pattern. For a certainly on recurring patterns, data considering multiple days and comparing similar timeframes would be necessary.
Identifying Outliers
Identifying outliers or significant variations from the trendline can be tricky in this limited dataset. However, if we consider significant jumps or declines in the exchange rate in a short period, we could potentially view them as 'outliers.' For instance, at 17:55:02, the rate jumped to 86679.47905 from 85621.14459 at 15:20:03 which seems to be a significant surge in comparison to the previous exchange rates.
That said, given the volatile nature of cryptocurrency rates, defining what constitutes an 'outlier' can be subjective and requires more specific criteria. It's important to note that this analysis doesn't consider any specific event or should the analysis also consider external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports. Those elements can often account for such anomalies in the data, making them less 'outlying' than they might initially appear.