2024-04-23 Lek News
2024-04-22
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
Firstly, upon initial overview of the data, it can be seen that the data points are collected at 5 minutes interval. This granularity of data provides a detailed look into the time series and its dynamics. Nevertheless, it is important to note that for the sake of analysis, sometimes it may be useful to aggregate data to a less granular level (e.g., hourly, daily) depending on specific questions or interests.1. Understanding the overall trend of the exchange rates:
The overall trend of the exchange rate in this dataset can be considered relatively stable, with minor fluctuations. The exchange rates mostly stuck around 0.01445 during the whole period. Although there were a few slight dips and rises, they did not significantly deviate from the general stability. This conclusion is reached simply by observing and comparing the numerical values, their maximum, minimum, and median values.
2. Identifying Seasonality:
With regards to the hourly data trends given, it's hard to identify any significant seasonality or recurring patterns in the changes in exchange rates. The exchange rate seems to fluctuate around a common point, 0.01445, with no clear cyclic behaviour. However, keep in mind that for a comprehensive understanding of seasonality in such data, a more advanced time-series analysis using decomposition methods or spectral analysis could reveal underlying patterns that are not immediately visible.
3. Noting any outliers:
There don't appear to be any major outliers in this data that would imply a significant or unexpected deviation from the overall trend. The slight ups and downs in the exchange rate are quite common and expected in financial times-series data. Although, it is worth mentioning that a statistically rigorous identification of outliers could require techniques such as moving average models, time-series decomposition, or even machine learning algorithms.
To Summarise, the provided dataset of exchange rates shows a fairly stable pattern with no clear seasonality or significant outliers. However, these observations are made from a rather high-level and general perspective. For more accurate and detailed insights, a more profound time-series analysis methods would be recommended.