2024-04-16 Unidad de Fomento News
2024-04-15
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
Overall Trend Analysis
Analyzing the dataset, the exchange rates (CLF) seem to have been fluctuating over the entire span of time, and it's important to analyse this in further granularity. A more detailed investigation would involve inspecting daily, weekly or monthly periods to identify potential trends. Using moving averages, especially a longer period one, could help smooth out short-term fluctuations and highlight longer-term trends.
Seasonality/Pattern Analysis
Isolating and understanding seasonality and recurring patterns often reveals important information about the dataset. To identify potential seasonal effects, the data points could be decomposed into several 'seasons', and statistical techniques could be applied to each season in order to detect patterns. Methods such as autocorrelation plots and Fourier transforms could be useful in determining if and where such patterns exist. If such a pattern is identified, this can lend powerful insights into the data's characteristic behavior.
Outlier Identification
Outliers are data points that are significantly different from other observations. They can arise due to variability in the data or errors. Outliers could affect the results of the data analysis, as they might lead to biased or erroneous results if not handled properly. Techniques such as box plots, scatter plots, Z-score, and IQR methods can be used to identify outliers. It would also be interesting to examine these outliers in context - whether they occur at specific times, or seem to be associated with particular events.
External Factors to Consider
Although the prompt specifically indicates not to, it's always important to factor in elements such as market opening/closing hours, weekends/holidays, or the release of key financial news and reports when analyzing financial time series data as these events might have an impact on the fluctuations. Ignoring these may risk overlooking important fluctuations or trends that are based on such events.
In concluding, a more exhaustive data analysis is required to draw significant correlations, patterns, and insights from this dataset. This would involve utilizing advanced statistical techniques and possibly expanding the dataset to have a broader perspective and accurate insights into the exchange rates.
