2024-05-06 Tunisian Dinar News
2024-05-05
Summary of Last Week
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Overall Trend Analysis
Although the dataset spans over a month, it still provides significant guidance on the trend of exchange rates. The trend gives a direction to the rates over the period being observed. In this case, for the month given, an upward trend in the exchange rate is observable. That is, the value of TND started off at 0.43301 on the 5th of April and ended at 0.43517 on the 3rd of May. Despite the usual daily fluctuations in rates, the general, underlying direction is upward within this period. The suggestion here is that TND has generally grown stronger as compared to the currency it is paired within this duration.
Seasonality and Recurring Patterns
Another important aspect to look at in time series data is the existence of seasonality or recurring patterns. From the given dataset, it is a bit challenging to conclusively ascertain the occurrence of seasonality within the period under review. This is partly due to the short period that the data covers. However, a closer look at the data reveals some regularity in fluctuations. The exchange rates seem to follow a certain pattern within the day. The values dip and rise at predictable times throughout the 24-hour period. The consistency of this pattern is disrupted on weekends, hinting at probable reduced trading activity which contributes to lower volatility during these periods.
Outliers in the Data
While a majority of the data seems to adhere to the trend and possible daily pattern, there are notable exceptions. On the 16th of April, the exchange rate drops significantly to 0.43721 at 16:00:02 from 0.44365 at 14:00:03. This sharp decrease, essentially an 'outlier', doesn't seem to follow the observed upward trend or daily pattern. It recovers steadily after that, but the reason behind this sudden drop remains unknown based on the data provided. While analyzing time-series financial data, such outliers should be carefully noted as they could potentially reveal valuable insights about unusual transactions or market conditions.