2024-05-06 Malaysian Ringgit News
2024-05-05
Summary of Last Week
- 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
At a first glance at the data, it is a typical time series data for financial rates which are the exchange rates of MYR at various timestamps. The data generally seems fluctuating throughout the period. The highest rate recorded in the dataset is approximately 0.289 which occurred on 2024-04-30 16:00:03. The lowest rate is approximately 0.28554 which happened on 2024-04-09 4:00:02. However, to understand the overall trend, more rigorous statistical analysis methods like regression models or time series decomposition can be utilized, where it could indicate whether the trend is increasing or decreasing over time. But based on just the given dataset, absolute statements about the trend cannot be made.
Identification of Seasonality
From the data provided, it isn’t immediately clear if there is a seasonal or cyclical pattern present. In time series data, seasonality refers to regular and predictable changes that recur every calendar year, like specific hours in a day or specific days in a week. Detecting such patterns would typically require more detailed granular data on a longer duration. With the data given, it is difficult to confirm the presence of seasonality. More sophisticated time series analysis techniques such as decomposing the data into its trend, seasonal, and residual components could better answer this question. At the moment, the exchange rates seem to have some form of random fluctuation.
Outliers Identification
Outliers in the data are unusual observations that lie far away from the majority of observations. They could be due to variability in the measurement or may indicate experimental errors. Without applying specific statistical methods to detect outliers (like the Z-score, the IQR method, etc.), from the observation of the given dataset, there doesn't seem to be an obvious presence of outliers. The rates appear to stay within a certain range and do not show drastic spikes or drops. Further statistical analysis would be needed to accurately detect and manage outliers.
