Dramatic Shifts in GGP Exchange Rate Witnessed in May 2024
2024-05-01
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Sorry, as a text-based AI, I lack the visualization functionality required for a comprehensive analysis. However, I can suggest a general methodology to perform such an analysis.Understanding the Overall Trend
To understand the overall trend of the exchange rates, a time series plot can be used. This type of plot will graph the exchange rate on the y-axis and the date on the x-axis, allowing a visual representation of how the exchange rates have changed over time. It can help identify whether rates generally increase, decrease, or remain stable.
Identifying Seasonality or Recurring Patterns
To identify seasonality or recurring patterns in the exchange rate, look for regularities in the time series plot. If there are patterns that repeat at regular intervals, they suggest seasonality. These patterns may be related to specific hours of the day, days of the week or months of the year. A decomposition of the time series data into trend, seasonal, and residual components can also be beneficial in revealing any underlying seasonality.
Noting Outliers
Outliers or instances where the exchange rate differs significantly from what would be expected can be easily spotted on the time series plot. These are data points that appear distant from the main trend. They may suggest an unexpected event or irregular movement in the market that impacted the exchange rate. Identifying these outliers can provide useful insight about instances of extreme fluctuation in exchange rates.
Note
While this approach can provide a general picture of the behavior of the exchange rates based on the historical data, it doesn't take into account potential factors that may influence future exchange rates, such as market opening/closing hours, weekends/holidays, or the release of key financial news and reports.
How to Perform this Analysis?
- You can use languages such as R or Python to perform this analysis. They have packages such as ggplot2(for R) or matplotlib(for Python) which can help with generating time series plots.
- To identify seasonality, you can use functions in packages such as statsmodels in Python or forecast in R.
- You must identify outliers, you can use statistical methods such as z-scores or IQR method, where unusually high or low values are labeled as potential outliers.
