2024-04-23 Ghana Cedi News
2024-04-22
Summary of Last Month
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Overall Trend of Exchange Rates:
The data provided contains exchange rates (GHS) for the time period covering 22 April 2024 with sequential time intervals. The data seems to reveal a slight fluctuation in the exchange rates throughout the given range of timestamps. In general, the movement of the exchange rate is subtle, with a light frequent pendulous shift suggesting considerable stability, not leaning towards a general increase or decrease over the given period. Minor deviations from the dominant level that range around 0.10175 to 0.1022 are observed.
Seasonality or Recurring Patterns in Exchange Rates:
Whilst observing the dataset provided, it is apparent that the exchange rate does not exhibit any prominent seasonality or recurring patterns within the specified timescale. The minor oscillations in exchange rates seem to be more random and do not represent a clear cyclical trend. However, the comprehensive examination of a larger dataset might reveal any possible seasonal patterns, as periods like daily market open and close times, or weekly starts and ends, may cause foreseeable fluctuations in the rates.
Outliers Noted:
Any substantial outliers or instances where the exchange rate differs significantly from the norm within the dataset aren't visible. All reported rates cluster tightly around the mentioned dominant levels, and no drastic peaks or troughs could be seen. However, there are minor peaks and troughs which represent the common fluctuation of the exchange rates. On larger analysis, these minor fluctuations might get classified as regular market play rather than particular outliers.
Conclusion:
To have a more in-depth and comprehensive analysis, a larger dataset with a broader time period may be required as it will provide a better context for investigating trends, patterns, and potential outliers. This could include higher-frequency data (e.g., minute or second data), longer historical data, or data at various granularities (e.g., hourly, daily, weekly data).