Summary of Last Month
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
- Standard Deviation:
1. Understanding the Overall Trend
As a time series dataset, this Bitcoin (BTC) exchange rate data moves in a generally increasing trend. The exchange rate started at around 69,660.95 and ended at a higher rate of approximately 75,773.87 on the last recorded timestamp. This suggests that there was overall growth or appreciation of Bitcoin's value relative to the USD or the currency in question during the period covered by this dataset. It should be noted however, the rates have seen some dip and rise during this period indicating market volatility.
2. Identification of Seasonality or Recurring Patterns
Regarding the seasonality or pattern, this dataset may not be ideal for determining seasonality due to the small timeframe covered (24 hours). Seasonality in time series analysis often refers to longer periods such as days of the week, monthly, or quarterly patterns that could be observed over one or more years. However, we can note small-scale fluctuations in the data where the value seems to be peaking during certain hours and decreasing slightly afterwards, suggesting intra-day seasonality where exchange rates may rise and fall in response to trading activities. To confirm this, a sophisticated time-series analysis methodology such as Auto Regressive Integrated Moving Average (ARIMA) modeling or a Fourier Transform can be useful.
3. Identification of Outliers
Outliers can usually be identified by any significant deviations from the overall trend or seasonality of the dataset. In this dataset, we can observe a significant spike that starts from the timestamp '2024-02-26 20:00:02' when the exchange rate went from 75,151.11 to 76,535.75 in 10 minutes which represents a significant jump, considering the usual intra-day fluctuations. This significant increase promptly followed by a decrease could indicate some form of external information affecting the Bitcoin's value for a brief period. Further investigation would be required to determine the root cause of these outliers.
As noted, these observations would need more context and a longer dataset to provide more solid conclusions or to capture patterns over longer periods. Also, a more granular analysis using methodologies tailored for time series data would also give valuable insights about this dataset.