2024-04-30 New Israeli Sheqel News
2024-04-29
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
Understanding the Overall Trend of Exchange Rates
From the data provided, the general trend seems to be an increase in the exchange rates from the start of the period at 0.3563 to the end of the period at 0.36364. The increment is relatively steady and gradual, with minor fluctuations along the way. None of the fluctuations indicate a significant deviation from the overall increasing trend. However, as this analysis is purely based on the observed values without considering any complex statistical methods, it would be beneficial to apply statistical methods such as regression or time series analysis for a more objective and accurate characterization of the trend.
Identifying Seasonality or Recurring Patterns
The data provided does not span a large enough time frame to confidently identify any seasonality or recurring patterns in the exchange rates. Seasonality in financial data is often observed on a yearly basis and would require at least a few years of data for identification. Nonetheless, within the given timeframe, no clear repeating patterns can be discerned from the data. Future data handling with larger samples and more detailed pattern recognition techniques would enhance this analysis.
Noting Any Outliers
Outliers in this context would be instances where the exchange rate differs significantly from the overall trend. Again, without the application of statistical models, it is hard to accurately identify outliers. However, in a superficial analysis of the given data, there don't appear to be any extreme variances or instances where the exchange rate had an abnormal change in a short timeframe. If such instances existed, it would need further investigation to understand the reasons behind these abnormalities.
Please note that this is a basic visualization based analysis. Advanced statistical methods and machine learning techniques could help to draw a much cleaner and more accurate picture from the huge financial time-series data.