2024-05-01 Rand 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
1. Understanding the overall trend of the exchange rates
The overall trend of the ZAR exchange rate fluctuates within a relatively narrow range throughout the period being assessed. The rate appears to slightly increase, yet remains close to 0.073 over most of the given interval, signaling a relatively stable exchange rate during this period. However, it's noted that near the end of the dataset, an abrupt yet temporary spike in exchange rate is noted, showing that the ZAR strengthens against the currency in question temporarily.
2. Identifying any seasonality or recurring patterns in the changes of exchange rates
Analyze of the given time series data doesn't point towards any evident seasonal fluctuations or recurring patterns. The volatility of the exchange rate remains consistent over time, without any discernable periodic increases or decreases. While there might be some hint of a diurnal pattern, it'd require further analysis with larger dataset to confidently assert any such seasonality.
3. Noting any outliers, or instances where the exchange rate differs significantly from what would be expected based on the trend or seasonality
- A significant outlier can be observed at the timestamp 2024-04-30 16:05:02 where the exchange rate reached a peak of 0.07358. This represents a substantial departure from the overall stable pattern seen throughout the dataset.
- Another notable instance is at timestamp 2024-04-30 16:10:02 when the exchange rate further rose to 0.07363.
Both these instances signify short-term spikes in the exchange rate of ZAR, deviating from the relatively stable pattern around 0.073. Factors causing these significant fluctuations are not available in the data provided. To understand the causes of such fluctuations, we would need additional data such as market news, political events, or other economic indicators that were not included in our dataset.
These are initial findings based on a visual inspection of the data. For a more comprehensive and accurate interpretation, it would be beneficial to apply quantitative time series analysis methods including decomposition of the trend and seasonality, identification and interpretation of autocorrelation functions, and prediction of future values based on the identified patterns. Such advanced analysis may provide additional insights into the underlying patterns and the factors influencing the fluctuations in the exchange rate.