2024-05-15 Ghana Cedi News
2024-05-14
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Data Overview and Preprocessing
Before starting to analyze the data, let's preprocess the data to make it suitable for analysis. The data appears to be in a string where each entry is a timestamp followed by the GHS currency exchange value at that time. Our first step would be to split these entries and convert them into a more suitable format, likely a pandas DataFrame where each row contains a timestamp and the corresponding GHS value.
Then we need to ensure that the timestamps are in the correct datetime format for further analysis. Missing or inconsistent data entries, if any, need to be handled accordingly considering the nature of the data and analytical objectives. It's also critical to check for any outliers or erroneous entries in the GHS exchange value.
Understanding the Overall Trend
To understand the overall trend, we will plot the GHS value over the time scale. This will enable us to visually comprehend how currency exchange values have changed over the time period covered in the data. A line plot would suit this purpose well, as the x-axis would represent the time stamp (showing progression over time), and the y-axis would represent the actual GHS values.
Identifying Seasonality
To identify any seasonality or recurring patterns, techniques such as autocorrelation plots or decomposition of the time series into trend, seasonality, and residuals can be utilized. Seasonality indicates that the series has regular patterns that repeat over time. If a series is seasonal, we should see high autocorrelations for the lags at the multiples of the seasonal frequency. The decomposition process allows to capture the seasonality in isolation and study it.
Outliers Detection
Outliers in the data could be due to extreme fluctuations in exchange rates caused by various economic scenarios or could be due to data entry errors. Boxplots or scatter plots of the data can highlight these outliers visually by identifying data points that fall significantly outside of the 'typical' range of values. Numerically, Z-score or IQR (interquartile range) can be used to detect outliers.
All the above analyses should give a comprehensive understanding of the dataset. Remember that time series data can be complex and it's often beneficial to consider multiple analytical perspectives to gain a complete understanding.
