2024-04-17 Zloty News
2024-04-16
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 given dataset describes the behavior of the PLN exchange rate over a specific period of time. From the given data, we can see that the exchange rate begins at 0.33602 at the start of the period and tends to fluctuate with minute changes throughout the period. It does not have a clear upward or downward overall trend. Instead, it mostly remains stable with minor fluctuations. The highest point that the exchange rate reaches is 0.33957, while the lowest that it drops is 0.33573.
Identifying Seasonality in the Exchange Rates
Time-series data, such as exchange rates, often exhibits seasonality, which means predictable and recurring patterns at regular intervals. However, from the provided data, it is difficult to make conclusions about any potential seasonality because the data does not cover multiple cycles of a potential seasonal period. Namely, the dataset would need to cover several years of data in order to detect patterns that repeat annually. Nevertheless, intraday seasonality might be a factor to consider given that foreign exchange markets can be subject to recurring intraday patterns due to various factors such as market opening/closing hours. Although in our case, the fluctuations seem to be more or less random without a clear repeating pattern within a day.
Identifying Outliers in the Exchange Rates
An outlier in time-series data is a data point that is significantly different from other observations. These occur infrequently but are usually due to specific events. From the observed data, there do not appear to be significant outliers, the changes in the exchange rate, when they occur, appear to transition smoothly rather than jumping abruptly from one value to another. This implies that the exchange rate does not fluctuate wildly and unexpectedly within the timeframe observed.