2024-05-06 Euro News
2024-05-05
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
Understanding the Overall Trend
From an initial evaluation of the dataset provided, we can see that the exchange rates fluctuate from approximately 1.45905 at the lowest to about 1.47574 at the peak. Over the course of the month, there does not appear to be a clear upward or downward trend, but rather some volatility and variation in exchange rates within the stated range. However, without applying a formal trend analysis or time series models, it is challenging to evaluate whether there are underlying increasing or decreasing propensities.
Identifying Seasonality or Recurring Patterns
When examining the dataset, it is also hard to identify clear patterns or seasonality in the exchange rates solely from the data provided. There are fluctuations that occur repeatedly, but these do not appear to be connected to a specific time or event, indicating a lack of discernible seasonality. Since exchange rates are influenced by a variety of factors, including global economic conditions, interest rates, and inflation, among others, it is difficult to infer a pattern without further information. It is recommended to perform a more sophisticated time series analysis, like ARIMA or Fourier Transforms, to pinpoint these patterns in a more scientific manner.
Noting Outlier
The dataset exhibits certain instances of sudden increase or decrease in the exchange rates, particularly visible on days such as April 16, 23, and May 03. These could possibly be outliers or instances where the exchange rate varies significantly from the general trend. That said, given the inherent volatility in exchange rates and without a more formal statistical analysis approach to identifying outliers, it is complicated to definitively classify these instances as outliers.
In conclusion, it's important to note that while we can interpret and extract general observations and potential patterns from the dataset, a more sophisticated and scientific approach like Machine Learning and Statistical Modelling would yield more accurate and significant results.