2024-05-06 Netherlands Antillean Guilder News
2024-05-05
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
Understanding the Overall Trend
From the provided dataset, it is clear that the ANG exchange rate is experiencing some fluctuations over the time period. It started at 0.75158 and ends at 0.75968. There are some noticeable increases and decreases in a short period of time throughout the data, indicating a level of volatility. It isn't clearly showing a specific trend for decreasing, increasing, or stabilizing, but there are some noticeable peaks and troughs which need a further seasonality and trend analysis separately.
Seasonality or Recurring Patterns
While examining the raw data, it is quite challenging to spot a clear seasonality or recurring patterns directly. However, by sorting and grouping the data according to the specific time of the day or week, it may reveal clear patterns not visible in the raw data. For a concrete conclusion, it is recommended to run time-series analysis methods such as auto-correlation function(ACF) and partial auto-correlation function (PACF) graphs or decomposition analysis.
Outliers
In a pure data-driven view, there are several points where the value increases or decreases dramatically in a short period, which may represent outliers or points of interest. For instance, the rate at 2024-04-10 08:00:03 is 0.75648 and then it increases almost immediately to 0.75894 at 2024-04-10 14:00:03. Other notable instances are found around 2024-04-08 and 2024-04-22 where noticeable changes occur. These instances could be due to various reasons ranging from market instability to high trading volumes during those times, or due to particular events that took place.
Concluding Remarks
While this data provides a comprehensive look at exchange rate fluctuations over specific points in time, further analysis and computations would be necessary for concrete conclusions. In order to make this data usable for valuable insights, it is recommended to take advantage of statistical and time-series analysis techniques with proper software support to interpret and forecast the possible trends, seasonality, and outliers clearly and correctly.