2024-05-08 Brazilian Real News
2024-05-07
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
On initial observation of the exchange rate trend provided, it appears that the rates are fluctuating within a narrow band. Throughout the time period, there isn't a consistent or defined upward or downward trend. Instead, the exchange rate undergoes periodic changes, suggesting that the market is rather stable within this timeframe. An in-depth statistical analysis or visual interpretation could provide a more quantifiable rate of change over time.
Seasonality or Recurring Patterns Identification
Identification of seasonality or recurring patterns typically requires a longer time-series data set, often spanning across several years. This allows capturing of the underlying repeated patterns associated with particular periods such as days, weeks, months, seasons or years. With the data provided, it's difficult to conclusively determine any long-term seasonality or recurring patterns. On a short-term basis, minor fluctuations could be attributed to recurring intraday activities often associated with market opening or closing.
Outlier Identification
Outliers in time-series data like exchange rates are values that are significantly different from the other observed values. They could be a result of irregular large purchases, drastic geopolitical events, or simply input errors. Often, they are characterized by sudden jumps or falls in the exchange rates that interrupt an otherwise consistent rhythm. Based on the data provided, there aren't significant changes in the exchange rates to suggest the presence of outliers. However, financial datasets with extreme complexities and irregularities often require advanced statistical tools or software for accurate outlier detection.
