Summary of Yesterday
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
- Standard Deviation:
Analysis of Time-Series Financial Data
If the time-series dataset represents hourly data, the following conclusions can be made:
1. Overall Trend
Overall, it is observed that the exchange rate initially showed a mild increase in the values before it started to increase at a slightly rapid pace from 1.71941 to a maximum of 1.7266. After reaching maximum value, a sharp decline has been observed which reaches a minimum of 1.72128. The data ends on a slightly increasing rate resting finally on 1.71916. Due to lack of context around this data, it isn't clear if this trend will continue or it's a transient trend.
2. Seasonality and Recurring Patterns
The dataset does not cover a longer timeline like a whole year, it’s challenging to identify any seasonality i.e weekly or annually patterns that might affect exchange rates. With the data provided covering less than 24 hours, any inference around seasonality might not be accurate.
3. Outliers Note
There are no significant outliers in the dataset. There is a sharp decline observed from a maximum exchange rate of 1.7266 to 1.72128 which quickly recovers, but without additional context or data, it's challenging to say if it's outlier or normal. It could be possible that a strong news event or market sentiment driven this drop and quick recovery which is common in forex markets.
Additionally, a sudden drop and then quick recovery is observed near the end of supplied data. The exchange rate drop from 1.72279 to 1.71938 within a few minutes, quickly recovered back to approximately prior level.
Again, the absence of additional context like market hours, news events makes it harder to explain these sharp movements or tag them explicitly as outliers.
It is important to remind that this analysis visually inspects the trends on the dataset provided and do not implement any robust statistical tests to provide more solid assertions. For a more comprehensive analysis, it is recommended to have a more extensive dataset, preferably covering multiple years. This would help to uncover daily, weekly and even monthly patterns and anomalies in the data. Moreover, it would be beneficial to consider other factors outside the dataset, such as the market opening/closing hours, weekends/holidays, or the release of key financial news and reports.