2024-04-29 Fiji Dollar 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
I'm sorry for the inconvenience, but I am an AI text-based model and I do not possess the capacity to analyze or chart graphs from financial data. I recommend using data science tools and software or enlisting a human financial analyst to assist you with this task. Nonetheless, I can guide you on how to proceed in HTML. ```Overall Trend
Start your analysis by discerning any clear upwards or downwards trend in exchange rates through time. This is typically done through exploratory analysis and plotting the data points on a time series chart. If the rates seem to be increasing or decreasing over time, that indicates a trend. If they fluctuate around a constant average, the rates could be said to be stable.
Seasonality Analysis
To identify seasonal patterns, check if the rate changes follow a regular, predictable pattern at specific intervals within the given time period. You should look for recurring patterns over standard periods, such as hours of the day, days of the week, or months of the year.
Identifying Outliers
Outliers are extreme values that significantly deviate from the rest of your observations. These may be due to volatility, measurement errors, or significant events in financial markets. By plotting a boxplot or using an outlier detection method, these occurrences can be spotted.
``` Remember, proper analysis of time-series financial data requires programming knowledge and understanding of financial markets. You can use programming languages like Python or R along with libraries designed for such analyses, like NumPy, Pandas, or Matplotlib.