Summary of Yesterday
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
- Standard Deviation:
The provided dataset holds time-stamped Omani Rial (OMR) exchange rates covering 1 day on 28th February 2024 with each timestamp approximately 5 minutes apart. The exchange rates permit us to explore the fluctuations throughout this period, analyze patterns and notable insights from this financial time-series data.
Analyzing the overall trend
Upon analyzing the data, the OMR exchange rates started at 3.51948. Towards the end of the day, the rate slightly increased, closing at 3.52558. This implies that the overall trend in exchange rates for the 28th of February 2024 was marginally increasing.
Seasonality or Recurring Patterns
In the context of intra-day exchange rates, seasonality or recurring patterns can often be linked to market open and close hours. The highest exchange rate recorded on this day was 3.53411, and the lowest was 3.51913. On an intraday trading perspective, there doesn't appear to be any clear recurring pattern on this particular day. However, a comprehensive identification of seasonality would require a larger dataset, preferably spanning across several weeks or months.
Identification of Outliers
Regarding outliers in this specific dataset, the quickest way in this time-series data to identify outliers would be spotting any significant jumps or drops within a short period. From the given data, there were no drastic spikes or drops in the exchange rate in these 5-minute intervals, indicating the lack of any notable outliers.
Nevertheless, the efficiency and accuracy of an outlier detection approach would drastically improve with a more extensive dataset.
Although external factors such as market opening and closing hours, weekends, and the release of key financial news and reports typically play a crucial role in influencing exchange rates, this analysis is based purely on the data provided and does not consider these factors.
This analysis provides an overview based on the exchanges rates of; one single day and to precisely determine patterns, trends, and seasonality, it's recommended to have a more extensive dataset that covers a longer period. This analysis serves as an example of what can be achieved with limited time-series data.