2024-05-01 Guernsey Pound News
2024-04-30
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Given the complexity of a comprehensive analysis, this response will focus on three primary parts: The Overall Trend, Seasonality/Recurring Patterns, and Outliers in the data.
1. The Overall Trend
Reviewing the data, the exchange rates appear to be in an upward trend. Starting at 1.7043 at the earliest timestamp and ending at 1.71651 at the last, indicates an overall appreciation. There are minor fluctuations along the trend where it alternates between periods of steady increase and slight depreciation. These small oscillations are common in financial time series due to constant changes in market conditions. However, these fluctuations do not alter the general upward direction of the trend.
2. Seasonality/Recurring Patterns
Since the provided data represents one day's worth of time series data, it may not be adequate to confidently observe any recurring seasonal patterns. Seasonal trends usually become evident after observing for longer periods like months or years. As exchange rates change mainly due to various economic events and market forces which may be short term or long term, more data might need to be analyzed for observing any seasonality.
3. Outliers
Outliers in this data set would be instances where there's a sharp deviation from the prevailing trend. At around 07:40:02 the rate jumps up significantly from 1.70362 to 1.70942. This could be due to some market event stimulating a sudden demand. Other than this, no major outliers are visible in this data set. But keep in mind that financial markets are complex systems where sudden changes can happen frequently.
Overall, this dataset represents fluctuating exchange rates throughout the day. It's a good representation of how foreign exchange markets work, continuously altering due to market transactions. However, further insights with more granularity could be derived with more data that includes market events, trading volumes, and extended periods.
