2024-04-18 Leone News
2024-04-17
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analysis of Exchange Rate Time-Series Data
Based on the given dataset, here's the comprehensive analysis.1. Understanding the Overall Trend
Upon analyzing the provided dataset, it is noticeable that the exchange rates (SLL) have remained consistent over the period. The rate has maintained a steady value of 7.0E-5 throughout all the timestamps. The data doesn't indicate any significant increases or decreases, hence suggesting that the exchange rate has been stable.
Identifying Seasonality or Recurring Patterns
Given that the exchange rate remains constant across the various timestamps analyzed, there are no observable seasonal or recurring patterns in the data. Normally, patterns could manifest as regular or predictable changes which recur every calendar year. However, in this instance, the rate stays the same across the entire timeframe, indicating a lack of variance, which in turn suggests there is no seasonality or recurring pattern within this dataset.
Outliers Detection
Outliers are extreme values that deviate markedly from other observations in the dataset. In this context, it would be an exchange rate significantly different from 7.0E-5. However, given the dataset, it appears there are no outliers. All provided data points are consistent with the overall observed exchange rate, and no significant deviation from the norm is noted. Thus, it can be concluded that there are no outliers in the dataset, within the context of the provided data.
In conclusion, given the constancy of the exchange rates for various timestamps, carrying out further advanced forms of time series analysis such as trend decomposition, autocorrelation checks, Fourier transform, or applying predictive modelling techniques may not yield additional insights beyond what has already been observed.