2024-05-01 CFA Franc BEAC 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 the exchange rates
The entry-level analysis of the data provided highlights that the exchange rate value appears to be quite steady throughout the period under examination. The majority of observations show a rate of 0.00224 with some occasional and minor variations up to 0.00225. It is important here to note that without a more substantial dataset, it is hard to discern any medium to long-term increase, decrease, or stabilization of exchange rates. However, based on this dataset alone, we may infer that the exchange rates have generally stayed stable in the considered timeframe.
Identifying any seasonality or recurring patterns
Seasonality or recurring patterns are a common feature in time-series data. Yet, in the dataset provided, such distinctive attributes are challenging to identify due to the lack of significant variability or trend in the exchange rate values. The observations stay relatively constant with only slight fluctuations. Without analyzing a more diverse dataset, extending across different periods (months, years), discerning a seasonal pattern is a challenging task.
Noting any outliers or significantly varying instances
From the given data, we don't observe any significant outliers or instances of exchange rate deviation from the typical 0.00224 and 0.00225. Furthermore, because of the lack of high fluctuations, it makes it difficult to detect anomalies based on this data. The uniformity in the dataset leads to the preliminary conclusion that the period represented might be a stable phase in financial terms. That's why no sudden jolts or dips are evident in the exchange rate fluctuations.
To summarize, the data represents a consistent trend in the XAF exchange rates with minimal variability. Determining seasonality or identifying outliers is challenging due to the uniform nature of the data. A more extensive dataset, spanning across a more extended period, might provide more insights into the seasonality and outlier detection patterns.