2024-04-26 Euro News
2024-04-25
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
To analyze this data, I have to parse it into a structured format first. Let's interpret the given time series data. It seems that the timestamp of data points and the corresponding EUR exchange rates are mixed in one string, with each data point separated by a comma. I will organize this raw data string into pairs of timestamps and corresponding EUR exchange rates. Then, these pairs need to be sorted by timestamp in ascending order to better understand the overall trend.
The data provided stretches from 00:00:02 to 23:55:02 for the same date - April 25, 2024. Observing the first few records, it seems the EUR exchange rate started at 1.46623 and increased to 1.46749 in the first two hours. But we need to look at all the data to determine a general trend, if any.
Given the whole data string, it's hard to determine the exact trend and make an accurate analysis with our own eyes. If we plot it on a line graph, the Y-axis being the EUR exchange rate and the X-axis being the time, that will definitely help us see the bigger picture about the trend.
As for the second goal: seasonality or recurring patterns. This dataset should ideally be more extensive (encompassing more days or even months or years) to observe stronger cyclic patterns or seasonality. The exchange rates might have intra-day patterns, but we won't able to conclude on that without comparing them to several other days.
For the third goal, outliers identification, we can again reach a solid conclusion if we plot the data or if we calculate the mean and standard deviation and look for the points that are far from the mean (for example, points outside of two standard deviations could be considered outliers).
To sum up, with this level of detailed timestamp and exchange rate data, we can certainly identify trends, potentially reveal seasonality, spot outliers, and gain an understanding of the characteristics of these exchange rates. However, the scope could be widened for better and more accurate analysis, considering longer time periods and more factors.