2024-04-17 Yemeni Rial News
2024-04-16
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
The dataset starts on 2024-04-16 00:00:00 and ends on 2024-04-16 23:55:03, which is roughly one day's worth of data. Over this day, the exchange rate oscillates between 0.0055 and 0.00553. It started the day at 0.00552, dropped down to 0.00551 before rising back to 0.00552. It then remained stable for long periods of time before dropping down to 0.0055 and then rising again to a peak of 0.00553. Afterward, the exchange rate generally stabilized around the 0.00552 level for the rest of the day. This suggests a slightly fluctuating market, with some level of volatility.
Seasonality And Recurring Patterns
Given this dataset covers only one day's worth of data, it is difficult to make any conclusions about seasonality or recurring patterns as these typically emerge over longer periods (weeks, months, years). The short timeframe only suggests a day's trading cycle and does not provide longer-term patterns.
Identification Of Outliers
Most of the dataset the exchange rate is stable at 0.00552 with some temporary downs to 0.0055 and ups to 0.00553 that could represent potential outliers in the dataset. These outliers may be due to increased volatility during periods of high market activity. However, seeing as this is real-world financial data, these larger changes are generally considered a part of the expected "noise" in the data rather than true outliers that may suggest data errors or extraordinary events. With only slight fluctuations, there are no extreme values which significantly deviate from the expected range.
Conclusion
The YER exchange rates on 2024-04-16 showed slight fluctuation but remained in a small range between 0.0055 and 0.00553. A few minor dips and peaks were observed but no significant outliers were detected. However, for more detailed and definitive insights about overall trend, seasonality and outliers, an extended timeframe dataset would be required.