2024-05-01 Pound Sterling News
2024-04-30
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Understanding the Overall Trend of Exchange Rates
Using the exchange rates data provided for the specific dataset, we can observe a fluctuating trend. The rates at the beginning of the dataset start at around 1.7164 and trace a complicated, fluctuating path to close finally around 1.71937 - a relatively small net increase. Throughout the period, the rates show fluctuations between different intervals, however, no definitive trend of consistently increasing or decreasing values observed. The data suggests more of a constantly adjusting market movement based upon the supply-demand dynamics at a given time. There's no apparent single-direction trajectory identified for the period under observation.
Identifying Seasonality or Recurring Patterns
With the current set of time-series data provided, it's slightly challenging to identify any seasonality or recurring patterns in exchange rate changes, as the data covers a single day (April 30, 2024). For a more comprehensive identification of seasonality or recurring patterns, we typically need a more extended time series date range that may cover multiple months or years. However, even within that one day, we notice some periods of relatively stable rates, which shows that there may be intraday patterns involved, which is typical in forex markets. These patterns are likely influenced by trading hours across different geographies.
Note on Outliers
An introductory examination of the exchange rates data does not show any obvious presence of significant outliers that would greatly deviate from the observed fluctuating trend. However, a more comprehensive statistical analysis would be required to identify potential outliers accurately. The detection of outliers can depend on the analytical methods used, which involves setting a certain threshold against the median or mean of the entire data set. With that said, at a glance, our dataset appears to contain variations typical of intraday forex market movements.
