2024-05-16 Aruban Florin News
2024-05-15
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
1. Understanding the Overall Trend of Exchange Rates
Upon analyzing your data, it appears that the overall trend of exchange rates is slowly decreasing. The initial rate starts at 0.75814 and by the end of the series, it ends at 0.75489. While there are instances of little upward swings, the general tendency throughout the period under review is on a gradual downward trajectory.
2. Seasonality or Recurring Patterns in the Changes of Exchange Rates
Regarding the identification of seasonality or recurring patterns, it's important to note that without considering external factors such as market opening/closing hours, or the release of significant financial news, this becomes a complex task. The data provided fluctuates within a small range repeatedly which can be seen as a frequent pattern but it would need further deep statistical analysis to confirm any form of seasonality. Some daily variation is visible, but it could be influenced by data noise.
3. Identification of Outliers
The data indicates few potential outliers where exchange rates appear to deviate more compared to its next values. Particularly several steep increases or decreases witness this. A specific example can be seen at 07:35:03 with a considerable decrease in the rate (from 0.75729 to 0.75568). Another instance can be seen around 09:00:04, where the value makes a big jump from 0.75688 to 0.75845. However, these outliers aren't extremely significant and not frequent, and they soon return to a closer range to the mean value.
Please keep in mind, detecting exact outliers in a time series data set requires rigorous statistical testing and cannot be robustly achieved without using such techniques.
For a future detailed and more accurate analysis, taking into account external factors or disaggregating the data over different periods (for example, separately analyzing different hours, days, or months) could enhance understanding and generate useful insights.