Summary of Last Week
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
- Standard Deviation:
The dataset given is a time-series data expressing the changes in SDG exchange rate at different timestamps. These timestamps are in the format YYYY-MM-dd hh:mm:ss, and the SDG exchange rate is denoted in decimal values representing the exchange rate at the timestamp. Before starting the analysis, it's important to parse the data correctly and methodically during the data preprocessing phase.
Overall Trend Analysis
Upon glancing at the data, there are some minor fluctuations in the SDG exchange rate but overall, it seems to be stabilizing around the 0.00224 - 0.00226 range. However, to draw a more precise conclusion, it would be recommended to plot a line chart using visualization libraries such as Matplotlib or Seaborn. This would help us to visually understand the exchange rate's overall trend within this period.
Seasonality and Patterns
After a thorough inspection of the dataset, no clear seasonality or recurring patterns have been identified. Generally, seasonality in financial time-series data indicates a fixed and known frequency, which does not surface from the provided dataset. However, further detailed analysis using a seasonal decomposition of time series by Loess (STL) or Autoregressive Integrated Moving Average (ARIMA) could reveal any underlying seasonality or recurring patterns that may not be readily observable.
With respect to this dataset, it does not contain any significant outliers where the exchange rate would differ notably from what could be expected based on the seeming trend. Even though, more robust techniques for outlier detection could include statistical methods such as Z-scores or the IQR method.
Please note this is a very simplistic analysis and without considering external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports, a comprehensive understanding may not be achieved. However, this can be a decent starting point for further, more detailed analysis.
This is a purely data-driven observation from the dataset provided and doesn't attempt to forecast any future rates. And, it's important to approach financial data with caution, as many external factors not present in the dataset can influence the trends and patterns.