2024-05-13 Euro News
2024-05-12
Summary of Last Week
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Without having a visual representation of this dataset such as a line graph, it's necessary to perform calculations to understand the trend, seasonality, and outliers in the exchange rates. 1.Understanding the Overall trend of the Exchange Rates
An overall trend from the dataset can only be determined in a high-level observation sense since a detailed statistical model like ARIMA or SES is not being used for the sake of this analysis. With that being noted, there seems to be a minor fluctuation in rates from 1.46578 to 1.4729. The dataset starts and ends at a high value while hitting low points around index 9 and a few after. This could suggest a volatile market which goes up and down quite often.
2.Identifying Seasonality of Exchange Rates
Due to the nature of financial markets, they do exhibit intra-day seasonality, especially within forex, taking into account different market opening hours worldwide. As such, an inspection of the dataset for every two hours per day could yield interesting insights. Unfortunately, without a visualization or more efficient machine-assisted calculation, it's quite hard to spot the seasonality within two-hours interval data points in the given dataset.
3.Noting any Outliers
As for outliers in the data, this would require setting a boundary condition that defines what constitutes an outlier for these particular observations. One commonly used technique is the inter-quartile range (IQR), which involves identifying values that fall below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. However, given the limited nature of the information provided, spotting such cases is not feasible without using the appropriate statistical tools.
It should also be noted that due to the financial markets' nature, what could be considered an "outlier" could in fact, be a market correction, reversal, or response to an economic indicator or event. Therefore, outliers in finance could provide significant evidence of a deviation from an established trend and should warrant further investigation.
In conclusion, determining these trends, seasonal patterns, and outliers within this dataset would best be performed with a statistical or data visualization tool for more accurate results. This analysis should also incorporate a larger dataset to generalize the patterns observed better and avoid arriving at premature conclusions based on insufficient data.