2024-04-22 Bahamian Dollar News
2024-04-21
Summary of Last Week
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analyzing the Trend of Exchange Rates
From observing the raw data, it seems the exchange rate of the BSD has been largely fluctuating within the range of 1.34885 to 1.37898 over the period shown. The highest recorded exchange rate during this period seems to be 1.37898, while the lowest recorded rate is 1.34885.
Although there were ups and downs throughout the period, the rates did not show a strong consistent trend of either increasing or decreasing. Instead, the rates fluctuated within a relatively narrow band, suggesting that the BSD exchange rate remained somewhat stable during this period.
Periodicity and Seasonality
Due to the nature of the data provided, it's difficult to identify any strong seasonality or recurring patterns without further in-depth analysis. It would require breaking down the data into different time frames, such as day, week, or month, and comparing the patterns within these time frames.
Spotting Outliers
Outliers in this data set would be values that stand out from the others because they are significantly higher or lower. It's not immediately obvious from the raw data if there are many significant outliers. However, the extremely high value of 1.37898 BSD on 2024-04-12 14:00:01 seems higher than most others and could be considered an outlier.
This kind of outlier analysis can be quite subjective and would likely require further statistical analysis to confirm. For example, we might consider a value to be an outlier if it is more than a certain number of standard deviations away from the mean.
By noting the overall trend, potential seasonality and any outliers, we've gained an understanding of this exchange rate dataset. However, for a more rigorous statistical analysis, we might want to use statistical and time-series analysis methods, and potentially utilize machine learning algorithms to predict future rates.