2024-04-19 Euro News
2024-04-18
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analysis Summary
It seems from the given time-series data that there are several characteristics and patterns that can be discerned. Please note that this basic analysis doesn't take into account external market influences, such as market opening/closing hours, weekends/holidays, or key financial news and reports impacting the exchange rate. Here’s a quick summary:
Overall Trend:
The movement of the exchange rate within this dataset is indeed quite dynamic, with both ups and downs in the exchange rate observable. However, the data doesn't show a clear long-term trend to increasingly higher or lower values. The exchange rate appears to fluctuate around an average level, suggesting a degree of stability over this particular period.
Seasonality and Patterns:
Though given the limited data scope, it's hard to determine any characteristics reflecting true seasonality, such as daily or weekly patterns. To observe seasonality, we would typically need to see clear, regular recurrences of similar rate shapes or forms on a daily, weekly, monthly, or even yearly basis. There may be less obvious cycles or patterns in this data - perhaps reflecting trading sessions or certain time-of-day effects - but we would likely need more data or detailed time-series analysis approaches to detect these.
Outliers:
Although most of the exchange rates data points follow a relatively smooth curve, there are a few data points that stand out from the rest. An algorithmic approach would help better identify these by comparing each rate or rate change to some measure of centrality (such as the mean or median rate) or dispersion (such as the standard deviation of rates). We could also look at rate changes that exceed usual thresholds or certain 'surprise' measures that compare the actual rate change to market expectations. But again, these would all require more data or additional market information.
In conclusion, while the dataset does show some interesting features and characteristics, a fuller understanding and prediction of future exchange rate movements would necessitate a more complex model that incorporates a wider range of data inputs and market factors.
