2024-04-29 Tunisian Dinar 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
Data Analysis
Before beginning the analysis, it's essential to note that accurate interpretation of time series data requires more than examining the given dataset. However, based on the dataset alone, below are the results.
1. Understanding the Overall Trend of the Exchange Rates
By looking at the time-series data from 29th March to 26th April 2024, there is a small overall upward trend in the exchange rate which means the value is gradually increasing. For instance, the rate began at approximately 0.43391 at the start of the period and ended around 0.43435, indicating a slight upward trend. However, this trend has not been steady and has experienced several fluctuations throughout the period.
2. Identifying Seasonality or Recurring Patterns in the Exchange Rates
Seasonality in time-series data represents predictable and repeating patterns over the period. In this dataset, clear seasonality patterns are not readily visible. The exchange rate varies significantly within days and across different days. The data does not exhibit a recurrent pattern that could point out to a specific time of the day or a particular day of the week, where the exchange rate consistently increases or decreases. Therefore, seasonality in this dataset is inconclusive with the given data.
3. Noting Outliers in the Exchange Rates
An outlier in this context is an exchange rate significantly different from the others. There are a few potential outliers visible. For example, values at time "2024-04-10 10:00:03", "2024-04-15 12:00:03", and "2024-04-16 10:00:02" where the rates were 0.43854, 0.44215, and 0.44431 respectively, seem to be significantly higher than the rates around these times. However, additional statistical analysis would be necessary to confirm these observations as true outliers.
Please note, the interpretation might change with more data or using statistical techniques to validate these preliminary observations, as they are made by direct observation from the dataset provided.
