2024-04-29 Turkmenistan New Manat News
2024-04-28
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 Trend
By viewing the data set, it can be observed that from the start till around mid-April, the TMT exchange rates show a generally increasing trend, from about 0.387 to nearly 0.394. However, post mid-April till the end of the dataset, there is a decline noted in the TMT exchange rate, with the rate falling back to around 0.390. Thus, the overall trend in this exchange rates dataset is a period of growth followed by a period of decline.
Identifying Seasonality
Seasonality refers to predictable and recurring trends or patterns that occur over an interval of time. However, financial time-series data, particularly at a granular level like intra-day as in this case, do not often exhibit strong seasonality. The reason is, exchange rates are influenced by an array of both predictable factors such as economic indicators and unpredictable factors such as geopolitical events. From the given dataset, no clear seasonality or recurring patterns are directly observable in the changes of exchange rates.
Noting Outliers
An outlier in a distribution is a number that is distant from any other number. In terms of this time-series data, a significant sharp rise or decline could potentially be an outlier, i.e., a rate that is significantly different from the rates around the same timestamp. Despite the volatility and a general lack of strong trend or seasonality, no clear outliers are evident from the raw data provided. However, to definitively identify outliers, a more rigorous statistical analysis or use of a boxplot or scatterplot might be needed which is not part of this analysis.
Summary
To sum up, this dataset shows some level of volatility in the foreign exchange rate. Though there's a general increasing and then decreasing trend within the given time period, there are no clearly identifiable seasonal patterns or outliers based on the dataset provided.