2024-05-10 Lithuanian Litas News
2024-05-09
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analysis Result
The first step in the analysis is to visualize the given data and categorize it into understandable metrics. In the given data, the timestamp seems to be recorded approximately every five minutes and the corresponding exchange rate value is given.
1. Understanding the Overall Trend
From a general glance at the data, it can be noted that the overall trend of the LTL exchange rate between the beginning and end timestamps is incredibly stable. The exchange rate starts at 0.46488 and ends at 0.46359. As the variation in the rate is less than 0.01, it shows a minor decrease but large stability. There is a gradual decrease from 0.46508 (around 01:15:02) to 0.46477 (approximately at 07:20:03). This trend later plateaus and then again drops from 0.46504 (at 03:55:02) to approximately 0.46303 (around 17:15:03). It then plateaus again until the end. We could categorize this as a somewhat weak downward trend.
2. Seasonality or Recurring Patterns
As this is time-series data, occurring over a short period (a single day), identifying long-term 'seasonality' or recurring patterns within this single dataset is not realistic. However, there were multiple fluctuations in the rates within this period. A typical pattern noticed is that there's a sudden drop, then few hours of stability, and then another sudden drop.
3. Outliers
In terms of outliers, it would be hard and inaccurate to identify any outliers or extreme results from this dataset without any additional indicators or benchmark to compare with the pattern. Further data extending around this date and comparisons with other typical dates or specific events might reveal potential outliers.
We didn't generate any forecasts in response to the request. Further, market events and financial news, which might have an impact, were not considered due to the constraints of the request. Overall, there seems to be a small decrease in the exchange rate within the data time frame.
Doing more in-depth analysis which includes longer running data could bring more robust trends, patterns and accurate forecasts.