2024-05-01 Swiss Franc 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
1. Understanding the Overall Trend of the Exchange Rates
The complete data is not available from this chat interface. However, based on the time-stamped extracts, we can derive preliminary insights. Generally, the CHF exchange rate appears to be experiencing minor fluctuations over the intervals presented. These fluctuations vary within a relatively limited range, implying no significant trend towards either a substantial increase or a considerable decrease in the exchange rate. However, it should be noted that this conclusion is drawn from limited data, and further comprehensive analysis is needed for a firm conclusion.
2. Identifying Seasonality or Recurring Patterns
From the time-series data available, it's hard to strongly state any clear recurring patterns or seasonality in the exchange rate. The data points reveal minor fluctuations but no prominent, recurring swing that would indicate a seasonal effect. More point-to-point inspection might be necessary to determine minute fluctuations otherwise unnoticed in the provided preview. Time-series decomposition could also benefit the analysis, but such an exploratory analysis requires entire dataset access—beyond the information at hand.
3. Noting Any Outliers
Outliers are extreme values that deviate from other observations on data, they may indicate a variability in a measurement, experimental errors or a novelty. In time-series data, they can be additive or innovative. Additive outliers are observations that bear unusual given the structure of a time-series dataset. Innovative ones impact the current and all future values of a time-series. Outliers can disrupt the working of models if not addressed properly. Based on the provided dataset, there doesn't appear to be any substantial outliers, as the data seems to remain in a specific numeric range. However, a more extensive analysis, including statistical methods for outlier detection (like the Z-score or the IQR method), will provide a more accurate picture of the presence and impact of outliers.
Note: This analysis assumes that the provided data is a simple time series without considering the influence of external factors. The actual fluctuation in exchange rates can be affected by myriad factors, including macroeconomic indicators, geopolitical events, and changes in market sentiment. As such, a thorough financial analysis may be different from this rudimentary overview depending on these external influences.
