2024-04-24 Serbian Dinar News
2024-04-23
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analysis of the Exchange Rate Trend
The exchange rate data provided is in a time series format consisting of values taken at evenly spaced intervals. Scanning through the datasets, it is noticeable that the exchange rate maintained a fairly stable movement throughout the period under review. The rate started at 0.01246 and drifted slightly downwards and upwards in minor fractions, signifying that the exchange rate during the period represented, has a somewhat horizontal or stagnant overall trend.
Seasonality and Recurring Patterns
In terms of seasonality or recurring patterns, the data provided does not show any distinctive cyclical pattern. The exchange rate remains fairly consistent throughout the observed period, with very minimal variation. This consistency suggests that there may not be any recurring patterns in the change of exchange rates based on the time of day or specific dates. Again, considering the stability and uniformity of the observed rates, no noteworthy seasonality can be identified.
Outliers within the Exchange Rates
Given the stability and consistency of the exchange rate throughout the dataset, any significant deviations would be considered as outliers. Such outliers can be spikes or troughs in the exchange rate that break away from the pattern of minor fluctuation displayed in the data set. In this particular dataset, there are no significant outliers present. All exchange rates observed have maintained a steady state, fluctuating between 0.01244 and 0.01249, which is a very narrow band. This consistency in the given time period signifies a pretty steady and stable exchange rate environment.
Additional Insights
Despite the data provided does not visibly show trends, patterns, or outliers, a more advanced statistical analysis could provide a deeper insight into the data. Techniques such as performing decomposition of the time series into trend, seasonality, and residuals or conducting a spectral analysis to identify any hidden cycles might potentially reveal more details. However, without incorporating external factors such as market opening/closing hours, weekends/holidays, release of key financial news and reports, the analysis will be limited.