2024-05-14 Kuwaiti Dinar News
2024-05-13
Summary of Last Month
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
An Overall Trend Perspective
From an initial glance at the data, one can generally determine that the exchange rate fluctuates within a certain range. However, to definitely determine whether the exchange rate increases, decreases, or remains stable over the time period noted, it would be helpful to plot the data points or use statistical measures such as average or median within certain periods (such as daily or hourly basis). Unfortunately, without performing these calculations or visualizations, it is difficult to definitively assess the overall trend in this exchange rate just based on the raw data.
Seasonality and Recurring Patterns
The establishment of any seasonal or recurring patterns in changes of exchange rates would also require further computational or graphical treatment of the data. Aggregating the data in units of time (such as hourly or daily) might reveal high-frequency patterns (such as daily or weekly cycles). Lower-frequency patterns (such as monthly or yearly cycles) could also be identified this way. Nonetheless, the presented raw data does not readily show any specific seasonality or recurring patterns.
Outliers
Outliers are typically defined as instances that differ significantly from the majority of the data. They can be caused by both random variation in the data or may indicate measurement errors or heavy-tail distributed events. Determination of any outliers in this dataset would again require further calculation, such as deviations from the mean or median beyond certain thresholds, strange jumps or falls, or unexpected stable periods. It's worth noting that if any outliers are found, further investigation would be needed to determine their causes, as they could be due to particular incidents happening at those times.
To summarize, this comprehensive analysis can only be performed efficiently with appropriate computational/statistical tools. Unfortunately, without conducting such analyses or creating data visualizations, it is challenging to draw clear conclusions simply by examining the raw data. Please consider using data analysis tools or programming languages to better extract insights from this dataset.
