2024-04-30 Trinidad and Tobago Dollar News
2024-04-29
Summary of Last Month
- 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
The overall trend of the Time Series Data (TTD) exchange rates in the dataset shows a consistent but gradual increase throughout the timeframe. The data recorded spans from 0.20047 to 0.20099. Initial readings at the start of the dataset show the values around 0.20047, and towards the end of the dataset, the values rise to approximately 0.20099. This indicates a growth rate over time. However, the rates fluctuate back and forth within a short range. These start from the smallest value of around 0.20047 and increase to the maximum observed value of 0.20099. Therefore, the rates remain relatively stable over the period shown.
Identifying Seasonality or Recurring Patterns
In terms of seasonality or recurring patterns, the dataset does not appear to have a clear-cut pattern due to the short-range fluctuations that it goes through. However, minor recurring patterns can be noticed where the rates witness an increase, stabilize a bit and then again increase. Such a cycle is seen repeating multiple times throughout the dataset. This might suggest that the TTD exchange rates do have a certain degree of repeatability in their behaviour, although this is not very pronounced.
Noting Any Outliers
Concerning outliers, the dataset seems to not exhibit any significant anomalies or outliers. An outlier, in this case, would be a rate that deviates significantly from the general gradation of the dataset. Given the trends observed, there don't appear to be any instances where the exchange rate differs significantly from the trend. Every change in the rate can be correlated to the trend of fluctuations as observed in the rest of the dataset. Therefore, it can be assumed that the given TTD exchange rate data is fairly consistent and does not contain any significant outliers.