2024-05-03 Loti News
2024-05-02
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
1. Understanding the overall trend of exchange rates
From a first glance of the given data, it appears the LSL exchange rate has minor fluctuations throughout the data set. However, to assess a more accurate trend, it would be necessary to apply a moving average or other similar smoothing technique to remove the noisy fluctuations and make the overall trend more visible.
2. Identifying Seasonality or recurring patterns
Identifying a potential seasonality or recurring patterns within the exchange rate data requires a detailed times series decomposition analysis. In the current dataset, it's challenging to observe any clear seasonality or repeating trends due to minor fluctuations. For a more accurate identification, advanced statistical models like an auto regressive integrated moving average model (ARIMA) or exponential smoothing state space model (ETS) can serve the purpose.
3. Noting any Outliers
Outliers in such data set can be referred to as those observations that lies an abnormal distance from other values. Given the limited range of exchange rates in the dataset provided, the differences are not so major and no clear outliers can be observed immediately. However, a more precise identification can be accomplished by boxplot method or using Z scores, for which the mean and standard deviation of the data will be calculated. Once the Z score is calculated for each value, those which falls outside the threshold range (commonly either below -3 or above 3) are considered as outliers.
It's important to note, while this analysis outlines the general trend, the presence or absence of seasonality, and potential outliers in the dataset, it does so without any external factors. Such an analysis could be made even more accurate and insightful with a thorough understanding of how external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports typically affect exchange rates.