2024-05-06 Hryvnia 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
Analysis of the Provided Time Series Financial Data
Looking over this dataset, a few things catch our attention immediately:
1. Trend of Exchange Rate
The overall trend in the exchange rate of UAH seems to be relatively stable. The rate fluctuates between 0.03443 to 0.03515. Without calculating the exact percentage change over time, this indicates that overall there hasn't been significant growth or depreciation observed for the considered period. The rate initially started declining from 0.03472 to reach a lowest of 0.03443 but increased to a maximum of 0.03515 and then again started declining to rest at 0.03478.
2. Detecting Seasonality
In a high-frequency time series data like this, where observations are made every few hours, seasonal patterns can often be daily (e.g., trading hours in a day) or weekly (e.g., weekdays vs. weekends). From the given data, it's not easy to identify a clear seasonality or recurring patterns due to the high level of noise and irregularity in the dataset.
3. Outliers
As the rates have remained in a very confined range, any rate going below or above the given range might be considered as an outlier. Visually inspecting the data, no such incident can be seen so we can say that no outliers are present in this dataset. For a more accurate determination, statistical methods can be employed to detect outliers, but that's outside the scope of this analysis.
Always keep in mind, this analysis is a general overview based on the data provided. For an in-depth understanding of the financial trends, more sophisticated time series analysis techniques along with a consideration of external factors would be required.