2024-05-08 Kuwaiti Dinar News
2024-05-07
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Overall Trend Analysis
The first step in analyzing the data is understanding the overall trend of the given exchange rate. Looking at the provided time series data, it can be observed that there is a general trend of increase in the exchange rates from a rate of 4.44998 at the start of the dataset to 4.47191 at the end. However, this increase is not uniform and has many fluctuations.
Identification of Recurring Patterns or Seasonality
Seasonality and recurring patterns in financial data can be a great help in understanding the movements of a particular variable. These patterns can be daily, weekly, monthly, or even annually. Upon a close inspection of the provided dataset, it is difficult to ascertain any clear patterns or seasonality without further analytical procedures such as decomposition of time series into trend, seasonality and residuals.
Outliers Detection
Outliers are the data points that diverge significantly from the overall pattern in a data set. They can be due to variability in the data or may indicate experimental errors. In financial analysis, they may highlight some extraordinary events that need to be investigated or controlled. In this dataset, one potential outlier may be the sudden increase in exchange rate to 4.47547 then a sudden decrease to 4.47087. This seems to be a deviation from the usual trend of the series and hence, can be considered as outliers.
In conclusion, it should be noted that this analysis is a basic and general understanding of the data. More complex and sophisticated statistical methods may provide deeper insights and more accurate detection of patterns and outliers in such time-series datasets.