2024-04-29 Rial Omani News
2024-04-28
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
To start off with the analysis, we need to first handle the raw data. The data provided is a univariate time series data, i.e., it consists of temporal timestamps and corresponding exchange rates. The first step would be to parse the timestamps and sort the data in increasing order of time.
1. Understanding the overall trend:
First, we can look at the overall distribution of the exchange rates to get an idea of how they are spread across different values. A box plot would help identify the median rate, the interquartile range, and potential outliers in the data. Next, we can plot the exchange rate value over time to directly visualize trends. An upward trend in the graph would indicate that exchange rates are generally increasing, while a downward trend would indicate a decrease. If the graph seems relatively flat, this suggests that exchange rates are largely stable over the period in question.
2. Identifying any seasonality:
To identify any seasonality or recurring patterns in the data, the method of decomposing the time series into its components is typically used. This breaks down the time series into the trend component, seasonal component, and residual component. If any repeats are noticed in the seasonal component, it indicates that there is seasonality in the data.
3. Noting any outliers:
Outliers in the exchange rates can be identified by various approaches. One simple way is using a boxplot, where rates that lie at an abnormal distance from others (outside of the whiskers in the box plot) are identified as outliers. Additionally, a more sophisticated method would be to fit a model to the data and determine points where the prediction error exceeds a certain threshold.
To perform this extensive analysis, you might require statistical software tools such as Python (with libraries such as pandas and matplotlib for data manipulation and plotting), and R (for statistical analysis). It's essential to note that these are high-level guidelines and might need to be adjusted based on the specifics of the data and the context in which they are being analyzed.
