2024-04-26 Mauritius Rupee News
2024-04-25
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Okay, as per your request, we will focus on patterns, outliers, and general trend analysis without considering any external influences. Here is an analysis of the provided dataset:1. Overall Trend Analysis:
The data provided for MUR (Mauritian Rupee) exchange rates from 00:00:02 to 23:55:02 on April 25, 2024, shows a slight fluctuating trend. From the available data, it can be inferred that the MUR rates mostly remained in the range of 0.02942 to 0.02954. The higher value occurred at the start of the day and fell slightly over the day, then increased towards the end of the day. This indicates a slightly negative trend for the day but it does not give clear evidence about a long-term trend. For that, similar data spanning across multiple days or months would be required.
2. Seasonality:
Based on the provided data, it's difficult to determine any strong seasonality or recurring patterns within the day. The fluctuation was within a narrow range, and no specific pattern can be decisively pointed out for this single day’s data. Seasonality patterns usually become apparent over larger datasets where recurring events and patterns can be recognized more intuitively.
3. Detection of Outliers:
In this data, an 'outlier' would be a value that deviates significantly from other values in the given period. Given that the data for this single day fluctuates within a narrow range, no significant deviation, thus no outliers, were observed. However, it is important to note that 'significant' is subjective and depends on the specific domain and the impact of such deviations on financial decisions. It may also be useful to consider a larger data set for more accurate outlier detection.
To summarize, this single-day data set for the MUR exchange rate on April 25, 2024, shows a slight fluctuating downward trend, without noticeable seasonality or outliers. For a more precise understanding of trends, seasonality, and outlier detection, a larger dataset spanning across multiple days, months, or years would be beneficial.