2024-04-29 Pa Anga News
2024-04-28
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. Overall Trend of the Exchange Rates
When looking at the dataset given, it appears that the exchange rates fluctuate over the period with slight incremental advancement. However, there don't appear to be drastic fluctuations in the dataset by considerable margins, hence the exchange rates maintain somewhat of a relative stability throughout the period. There are intervals when the exchange rate has dipped, however, these instances have been counterbalanced by the values subsequently rising again reaching near previous levels or occasionally even surpassing them. Such continual rebound symbolizing an underlying degree of resilience in the given exchange rates.
2. Seasonality or Recurring Patterns
Analyzing seasonality in financial time-series data often involves efforts to recognize and account for predictable and repeatable patterns that occur at regular intervals due to seasonal factors. In the provided dataset, it appears that the fluctuations in the exchange rate do not directly correlate with a specific time of the day or month, prompting conclusions that no clear pattern or seasonality stands out. Yet a hypothesis of a particular seasonality present would require broader datasets and more advanced time-series methodologies to take into account minute variations.
3. Outliers in Exchange Rates
Outliers are typically the individual values that deviate significantly from the majority of data. In this dataset, because the exchange rates do not show drastic shifts, identifying potential outliers would require statistical methods like the use of the Interquartile Range (IQR) or Z-scores. Based on basic observation though, there look to be no drastic spikes or drops which could be easily considered as outliers in the given dataset.
For a more in-depth analysis, using statistical methods alongside data visualization tools like line graphs and box plots would enable more precise identification of trends, seasonality, and outliers. Also, a larger dataset spanning a more extensive timeframe would further aid in understanding the exchange rate behaviors and identifying significant patterns.
