2024-04-17 Uzbekistan Sum News
2024-04-16
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Given the dataset, please note that the exchange rate for UZS remains constant at 0.00011 across all the timestamps provided. Essentially, the dataset does not exhibit any variations or fluctuations in the exchange rate. Therefore, the normal parameters like seasonality, trend analysis, and volatility that are typically considered while analyzing time-series data might not be applicable to this dataset. However, here are some general insights extracted:
Overall Trend
There is no particular trend noticeable in the exchange rates as the value has remained constant at 0.00011 regardless of the timestamp. In usual scenarios, the trend analysis would provide critical information about whether the rates are increasing, decreasing, or staying stable, however, this dataset lacks such variability.
Seasonality
In this dataset, seasonality or recurring patterns cannot be identified because there is no variation in the exchange rates over different time periods. Usually, in a time-series dataset, we look for certain patterns or changes that occur periodically, but in this case, the constant value of the exchange rate eliminates the possibility of finding seasonal variations.
Outliers
Given the unchanging nature of the UZS exchange rate in this dataset, the concept of outliers becomes irrelevant. In a traditional context, 'outliers' refers to data points that deviate significantly from the rest of the observations. But here, since every single data point is identical, we don't have any outliers.
Conclusion
Overall, this dataset of UZS exchange rates shows no alterations, making it challenging to perform a comprehensive time-series analysis. Without any differences in the exchange rate over time, it becomes impossible to determine aspects like overall trends, seasonal patterns, and outliers. For a more informative financial analysis, a dataset with more variability and larger time-series changes would be more beneficial.
