2024-05-20 Azerbaijanian Manat News
2024-05-19
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
1. Understanding the overall trend of the exchange rates
Having reviewed the information on the trends in the exchange rates over the specified period, it can be deduced that there's considerable variability in the exchange rates over the provided timestamps. The exchange rate begins at 0.81097 and ends at 0.79853, indicating a slight decrease over the period. On some dates, the exchange rate increases marginally while on others, it decreases
2. Identifying any seasonality or recurring patterns in the changes of exchange rates
While scanning through the data, no distinct seasonality or recurring patterns in the exchange rates could be identified. The data does show some fluctuation in value over time but is not consistently repeating, therefore suggesting potential randomness in the exchange rate changes. However, a complete conclusion on the seasonality needs a more advanced and sophisticated time series analysis.
3. Noting any outliers, or instances where the exchange rate differs significantly from what would be expected based on the trend or seasonality
There are some instances of notable spikes or dips in the rate. For instance, on 2024-04-22 at 06:00:02, the exchange rate shot up to 0.82127, while the next time stamp recorded a significant drop to 0.80806. Also, there are instances when the exchange rate dips lower, for example, on 2024-05-16 22:00:02, when it went as low as 0.78445. These outliers significantly deviate from the expected trend and do not align with the general movements observed across the period.
Remember, the analysis is based on the provided data and any external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports were not considered. This is a basic time series analysis, to get more accurate and detailed insights, advanced models and methods like AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing, etc., can be applied.