Summary of Last Month
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
- Standard Deviation:
Overall Trend of the Exchange Rates
Looking at the dataset from a macroscopic perspective, we notice that the exchange rates do not remain constant, they fluctuate over time. However, the fluctuation is quite small, which means the rates are relatively stable but not completely so. The exchange rate started at 1.71103 and ended at 1.71224. It is important to note that examining an overall trend merely from an opening and closing perspective is a bit simplistic, but it appears that there is a slightly upward trend in the currency exchange rate.
Seasonality and Recurring Patterns
Identifying seasonality or recurring patterns in this set of data is a complex task because it requires a large dataset (e.g., monthly or yearly data). With just a one-day dataset, it becomes particularly challenging to identify any seasonality changes or significant recurring patterns. However, more thorough seasonality or pattern analysis would need data spread across multiple cycles or periods (e.g., spanning through several years).
Outliers in the Exchange Rates
By definition, an outlier refers to an observation that lies an abnormal distance from other values in a random sample from a population. In time series data like this, outliers could result from extreme events or errors. From a cursory review, there does not appear to be significant deviation in the values given (ranging between 1.70984 and 1.71341), suggesting a lack of obvious outliers. To be absolutely sure, detailed statistical tests can be conducted to formally identify outliers.
It is important to recognize that financial time series data like this exchange rate data solely reflects the outcomes of underlying behaviors and events in the financial markets. It is influenced by countless variables, including changes in global economic outlook, political developments, trader sentiment, and much more. As such, while it can be analyzed to identify past patterns, changes, and anomalies, it should be understood in the context of these underlying factors. Moreover, future trends and patterns may not necessarily mirror the ones identified in the past data due to the ever-changing nature of economic and financial markets.