2024-04-22 Lithuanian Litas News
2024-04-21
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
1. General Trend of Exchange Rates
The dataset shows a fluctuation in exchange rates over time from 2024-03-22 to 2024-04-19. The exchange rate started at approximately 0.45934 and ended at approximately 0.46539. Whilst there is an overall increase in the exchange rate during this period, the changes are not consistent. Rates rise and fall at different times, without any clear upwards or downwards trajectory. Despite the fluctuations, there isn't a considerable deviation from the starting and ending points indicating a rather stabilizing trend over time.
2. Seasonality and Recurring Patterns
Identifying specific recurring patterns or seasonality in time-series data requires a detailed and complex analysis. In this case, given the relatively short time frame of the dataset and the occasional inconsistencies in the times at which the data points have been recorded, it makes it difficult to directly identify a clear seasonality or recurring patterns in the exchange rates.
3. Outliers and Significant Deviations
- One noticeable deviation occurs on 2024-04-10, where the exchange rate significantly goes up to 0.46222 in contrast with the previous rate of 0.45917.
- Another noticeable increase occurs on 2024-04-12, where the rate rises to 0.46474 from the previous 0.46364.
- A significant drop is observed on 2024-04-03, with the rate dropping to 0.45781 from the previous 0.45957.
These values deviate from the surrounding data points, marking them as potential outliers. Though this rise and fall could be typically seen as anomalies, without any specific contextual information it is not advisable to conclusively consider them as impactful outliers.
4. Impact of External Factors
While the analysis requested specific disregard of external factors, it is critical to note that exchange rates are intrinsically linked to a wide range of events and conditions. These can include changes in commodity prices, inflation rates, political instability, economic performance, and interest rates amongst others. Even within the short time span of our dataset, it is probable that fluctuations in the rates can be linked to such factors.