2024-04-22 Mexican Peso News
2024-04-21
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
1. Understanding the Overall Trend of the Exchange Rates
Based on the time-series data provided, the MXN exchange rate seems to show a slight upward trend over the entire period observed. We begin with an exchange rate of 0.08084 on 2024-03-22 and end with an exchange rate of 0.07992 on 2024-04-19. However, the increase isn't uniform, and there are significant fluctuations within this period. It's important to note that these observations are put forward under the assumption that the provided data represent the entire data set and not a specific subset.
2. Identifying Seasonality and Recurring Patterns
At a glance, the data doesn't demonstrate clear seasonality or recurring patterns. However, a more detailed analysis, such as applying a Fourier transform or Autocorrelation function, would be needed to confirm this as these tools can help identify underlying cyclical patterns in the data. It's important to note that such patterns might not be immediately visible, especially in financial times series data, and require more sophisticated methods for their detection.
3. Outliers in The Exchange Rates
While it's challenging to confidently identify outliers without the explicit data visualization or without performing a statistical test such as Grubbs's test, the data point on 2024-04-18 with a much lower than usual exchange rate of 0.07866 may be considered an outlier. This is because this rate is considerably lower than the rates observed immediately before or after this timestamp. However, this would need to be confirmed with a statistical outlier detection method.
Remember, outliers could be due to several reasons in financial data including significant financial events in the market or perhaps even due to some data entry or gathering error. Therefore, further investigation is often required to understand the reasons behind each outlier.