2024-04-18 Bahraini Dinar News
2024-04-17
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
1. Overall Trend Analysis
The data for the exchange rates from the timestamp '2024-04-17 00:00:02' to '2024-04-17 23:50:02' contains fluctuations, however, it is crucial to identify the overall trend. The exchange rate starts at 3.66726 and end up on 3.64962, this implies a decline in the exchange rate over the timeframe. However, this trend isn't always linear, there are periods where the rates increase temporarily before continuing to decrease.
2. Seasonality Analysis
Upon analyzing the data, it was not feasible to identify a distinct seasonal or recurring pattern with the provided dataset. The dataset shows a single day's worth of data, April 17, 2024. Seasonality patterns typically require a more extended data set, often several months to a few years, in order to observe regular and predictable changes that recur every calendar year. As usual, when dealing with intra-day data, there could be patterns related to market opening/closing hours which could have been ignored according to the given instruction.
3. Outliers Analysis
Outliers in a dataset are values that are significantly higher or lower than the majority of the data. To identify these in our dataset, we would need to look at points where there is a substantial jump or drop in the exchange rate that isn't in line with the overall trend. One method to calculate this is by determining the standard deviation of the entire dataset and highlighting points that lie beyond a certain threshold. Outliers could represent anomalies in the data collection process, or they could potentially highlight critical events in the wider financial market. However, given the dataset and without specific criteria/threshold for outliers identification, it is challenging to point out potential outliers from the data provided accurately.
