2024-04-18 Quetzal News
2024-04-17
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 of the Exchange Rates
The overall movement for the exchange rate, which appears to be more or less stable. However, there are very minor increasing and decreasing trends that alternate over different stretches within the dataset. It initially increases slightly from 0.17762, reaches a peak at 0.17781 then drops to the lowest point, 0.17720, until it gradually recovers and goes through similar dips afterwards.
It is important to note that these are very subtle and fractional changes and the exchange rate is maintained at around 0.177 within the dataset.
Seasonality or Recurring Patterns
From the provided dataset, identifying a clear, repeating seasonality or recurring pattern is challenging due to the small changes in the exchange rate and the limited scope of the dataset. The data show a series of crests and troughs, but the frequency does not appear to be regular enough to state with certainty that this represents a consistent cyclical pattern.
The changes occur in a seemingly random manner, suggesting that the exchange rates might be random walk series. To confirm, an autocorrelation plot or ADF test would need to be conducted, however, with the limited data and without conducting these tests, it is still difficult to make a definite statement about seasonality.
Outliers
The exchange currency rate remained relatively consistent with the given dataset, considering the small range of the exchange rate between 0.17715 and 0.17781. Therefore, it is not straightforward to identify any major outliers from visual inspection alone.
However, for a more precise check, a boxplot or using the Z-score could help identify the outliers better. For instance, any points that lie below Q1 - 1.5*IQR or above Q3 + 1.5*IQR, where IQR is the interquartile range, may be considered outliers. Furthermore, data points with a z-score (a measure of how far a point is from the mean in standard deviation units) above 3 or below -3 are also typically considered outliers.
However, it is necessary to take into account that legitimate extreme values may be deemed anomalies by these methods. Here, such extreme values could suggest significant fluctuation or volatility and may carry vital information.
