2024-05-02 Mauritius Rupee News
2024-05-01
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analysis of MUR exchange rate dataset
The user has provided a dataset containing time series data for exchange rates of MUR (presumably Mauritian Rupee). The exchanges rates are recorded at different periods across different times of the day. However, it is essential to note that my analysis does not consider external factors such as market operations, major financial announcements, or public holidays/weekends. Also, I won't provide any forecast estimations for future rates but only appeals to understand the historical data trend
General Trend
After analyzing the data, the overall trend of MUR exchange rates on the mentioned date appears to have marginal fluctuations but leaning more towards a slight decrease throughout the day.
There was a peak at 0.02972 early in the day, and then the rates appear to fluctuate just slightly throughout the day, more inclined towards a slow decrease. By the end of the day, the exchange rate had gone down to the lowest at 0.02958 marking a slight decrease overall.
Seasonality or Recurring Patterns
Based on the data provided, it might be difficult to make any robust claims about seasonality or recurring patterns since the data covers only a single day. However, there appears to be minimal variability on this particular day.
If additional data across multiple weeks, months, or years were made available, it would be possible to detect any seasonality or recurring patterns that might be present in the data.
Outliers
The exchange rates across all the timestamps on this day typically fluctuate between 0.02971 and 0.02959, with very few instances of slightly higher or lower values. Therefore, it seems there are no significant outliers in this dataset where the exchange rate differs significantly than expected.
It's essential to note that due to the nature of the data (financial and time series), a deeper analysis, possibly using statistical methods or machine learning techniques, might reveal patterns or outliers that are not immediately apparent with a preliminary overview.