Summary of Last Month
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
- Standard Deviation:
1. Understanding the Overall Trend
The dataset provided represents the QAR exchange rate recorded at different timestamps. The dataset spans for a 24 hours period on February 26, 2024.
Initially, at 00:00:02 the exchange rate was 0.37111, and in the end, at 23:55:02 it was 0.3709. These values suggest that overall exchange rate fluctuated in both directions over the period and shows a slight decrease from the start to the end of the 24 hours time frame.
However, the highest rate reached was 0.3716 and the lowest rate was 0.37084. The rate wasn't all the time decreasing or increasing, there are periods where it increased, then decreased and vice versa. The exchange rate graph will look like a zigzag.
2. Identifying Seasonality or Recurring Patterns
Without the help of visual aids such as graphs or more detailed descriptive statistics, it is challenging to assertively discern any type of seasonality. But by analyzing data it seems that there are no apparent recurring fluctuations that would suggest a recognizable pattern within this 24 hours period. It's generally better to analyze data over a more extended period to identify seasonality. This is because seasonality often happens over weeks, months or quarters, rather than individual days.
3. Noting Outliers
Analyzing the data, a number of instances were noticed where the exchange rate differed significantly from the rates immediately before and after. For example, the significant jump from 0.37108 at 01:40:02 to 0.37123 at 01:45:02, and another considerable fall from 0.37109 at 15:25:03 to 0.37101 at 15:30:02. Yet these movements aren't drastic and fall within the overall range of exchange rate values for the given day. Hence, they may not be considered as 'extreme' outliers.
Speaking about outliers, here we should also consider whether to label some observations as outliers based on their absolute values or based on their relative change compared to the previous observation. The first approach might not be beneficial in this case, as all observed QAR exchange rates are within similar ranges – no drastic highs or lows are observed. However, the second approach might detect some points worthy of additional attention.
In summary, your data demonstrate fluctuations throughout the day, with several significant exchanges that could be considered outliers. Further detailed statistical analysis, such as standard deviation or quartile analysis, may provide more insight into the extent of these fluctuations and the presence of outliers.