2024-05-03 Tunisian Dinar News
2024-05-02
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Below is the comprehensive analysis of the provided dataset. The data contains time-stamped records of exchange rates in TND.
1. Overall Trend
Based on analysis of the dataset from 2024-05-02 00:00:02 to 2024-05-02 23:55:02, it appears that the exchange rates exhibit a decreasing trend over the period shown. This is indicated by the fact that the exchange rate starts at 0.43605 at the first timestamp and ends at 0.43449 at the last timestamp. However, this overall decreasing trend is accompanied by many small fluctuations of increase and decrease, indicative of the dynamic nature of the currency market. Please further validate this with a plotting of the data.
2. Identifying Seasonality
Seasonality or recurring patterns in the data potentially depend on several factors such as trading patterns and economic cycles. In the given dataset, as it represents data for a single day, seasonal trends might not be clearly visible. For an accurate determination of seasonality, a more extended dataset encompassing several weeks, months, or years is typically required. One can then perform a time series decomposition analysis to identify the seasonality component. Without this extended data and strictly within the given dataset, clear patterns or cycles of rate changes are not evidently discernible.
3. Outliers
Outliers in this financial time series data can be judged based on sudden, abrupt changes in rates that do not align with the general pattern of fluctuations. In the given dataset, discerning outliers through simple observation might not be an effective approach due to the high frequency of data records and changes in exchange rates. To effectively detect outliers, statistical techniques such as Z-Score, Modified Z-score, or the use of Interquartile Ranges (IQR) can be applied. These methods require additional calculations and are not executed within this response. To detect these outliers, one should plot an exchange rate time series graph and detect the sudden spikes or falls.
Remember, external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports can significantly influence these trends and outliers.