2024-05-16 Iranian Rial News
2024-05-15
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
First, we'll look at the dataset you provided. It appears there is no variation in the dataset since every timestamp is associated with the same exchange rate value (3.0E-5). Thus, all standard analysis techniques such as trend, seasonality, outliers, etc., will not provide any meaningful insights for this dataset.
In order to provide a considerable evaluation, I will demonstrate what would have been done should the data show some variability. The data in real scenarios usually has some variations. The demonstrated result will be based on the assumption that the dataset shows variation and not limited to one value.
1. Understanding the Overall Trend
A common way to identify the overall trend in time series data is by creating a line plot of the data over time and applying a trend line. If the exchange rates generally increase over time, the trend line will have a positive slope. If the rates generally decrease, the trend line will have a negative slope. Finally, if the rates remain relatively stable, the line will be flat. Note: In the context of the dataset you provided, however, as all exchange rate values are the same, we can say that the rate has remained stable over time.
2. Identifying Seasonality or Recurring Patterns
In order to identify any seasonality or recurring patterns in the exchange rate changes, we would perform a seasonal decomposition of the time series data. This might involve identifying cycles or repeating patterns that occur over regular intervals. However, with all exchange rate values being the same, we can't identify any seasonality or recurring patterns in this dataset.
3. Spotting Outliers
Outliers are the individual values that deviate significantly from the overall pattern of data. They can be identified using various statistical techniques, such as Z-scores, IQR method, or even visually identified on a boxplot or scatter plot. In the dataset you provided, since there is no variation in the exchange rate values, there are no outliers.
In conclusion, the dataset you've provided doesn't give much scope for any kind of analysis because the exchange rate remains constant. To have a more insightful analysis, the data should contain variations in the exchange rate.