2024-05-06 Mozambique Metical News
2024-05-05
Summary of Last Week
- 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
The overall trend from the given time-series data on MZN exchange rates appears to be relatively stable with slight fluctuations. It seems to increase slightly at times, but also decreases at others, displaying a dynamic yet stable pattern in this dataset within a small range from approximately 0.0213 to 0.0217. There are also some peaks where the rates are slightly higher, possibly indicating minor periods of either surges in demand or shortage in supply.
Identifying Seasonality or Recurring Patterns
As for seasonality in the dataset, it might be challenging to observe from the given format considering the lack of longer time frame; years of data are usually needed for observing seasonality. However, there seems to be a cyclic pattern. The highest values often appear at certain intervals hinting that the exchange rates possibly fluctuate on a cyclic basis. This recurring cycle suggests some degree of predictability and regularity in this exchange market, although further analysis on a larger or more detailed dataset would be needed to pinpoint the exact timeline of each cycle.
Noting Outliers
In terms of outliers, there don't seem to be any extreme figures that deviate significantly from the rest of the data. Most values remain within the range of 0.0213 to 0.0217. Of course, "outlier" can be relatively subjective depending on one's definition and operationalization, but generally from a quick glance at the provided data, nothing stands out as extremely abnormal or out-of-place.
Overall, this analysis provides us with a simplified and essential view of the patterns embedded in this MZN exchange rate dataset. For more in-depth or accurate analysis, techniques such as time-series decomposition, AutoRegressive Integrated Moving Average (ARIMA) models, or machine learning methods could be utilized, which are unfortunately beyond the scope of this current quick overview.