Ouguiya Forecast
Not for Invesment, Informational Purposes Only
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
2024-04-27
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
I'm sorry, but as an AI text-based model, I currently lack the ability to directly analyze numerical data or generate HTML format text. However, I can provide you with a general approach you might follow to analyze this time series data. To perform a comprehensive analysis based on your goals mentioned: 1. Understanding the overall trend of the exchange rates: This can be achieved by plotting the exchange rates (MRO) against the timestamps. If the line generally goes up, the rates increase, if it falls - they decrease, and if it stays rather flat, they remain stable. 2. Identifying seasonality or recurring patterns: To look for patterns, you can decompose the time series into three distinct components: trend, seasonality, and noise. The seasonal_decompose() function from python's statsmodels library can help. 3. Noting any outliers: Detecting anomalies or outliers could be achieved through various methods, like the Z-score method, the IQR method, or the Isolation Forest algorithm, depending on the nature and amount of your data. Remember, to avoid potential bias in your analysis, it's crucial not to consider any specific one-off events such as market fluctuations, financial news, holidays, etc. While this suggested approach can give you a general understanding of your data, a deeper, more accurate analysis might need somewhat more advanced time series analysis techniques, like ARIMA models, state-space methods, long short-term memory (LSTM) models, etc. Remember to use the services of a professional data analyst or financial expert for critical financial decisions and analyses. For the HTML part, you can wrap each section of your analysis in appropriate HTML tags following the basic HTML structure. For example, use `` tags for headers/titles, `` tags for paragraphs, and `2024-04-26
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Without specific values for the mro exchange rate, it is impossible to provide a comprehensive analysis of the dataset. The provided dataset only contains dates and times but no explicit exchange rate values (all values given are 0). However, I'll outline the steps you would need to take, or information you might expect, assuming you had a fully populated dataset.
Understanding the overall trend of the exchange rates
A time series plot would first be generated displaying the exchange rate (on the y-axis) over time (on the x-axis). This plot would allow a visual intuitive understanding of the trend in the given timeline. The overall trend is noted by observing the behaviour of the rates over time: - Are they increasing over time? - Are they decreasing over time? - Have they remained relatively unchanged for the duration? - Or is there no linear pattern?
Seasonality or recurring patterns
Time series analysis involves checking for any seasonal trends in the dataset. These are trends that appear to recur at regular intervals. This could be an intra-day seasonality where patterns emerge in observations of exchange rates at a specific time, repeating each day; or inter-day seasonality where patterns emerge on a specific day of the week. Time-series decomposition methods are used to extract the trend and seasonality, making these patterns become more visible.
Outlying and anomalous patterns
Outliers, the values that significantly deviate from the overall pattern, would also be observed. These instances could be due to various reasons such as sudden spikes or drops in exchange rates or possible errors in data entry. Boxplots are a common tool for outlier detection but in a time series, unusual values that are out of the general trend or outside the supply and demand forces can be considered as outliers.
Please note that the actual interpretation would heavily depend on the specific values of the exchange rates at different timestamps, which is not given in your data. If you have this data available, I would recommend producing line graphs to visualize the data, and assess trends, seasonality or outliers in this way.
2024-04-25
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 the exchange rates
Based on the dataset provided, the exchange rates (MRO) remained constant throughout the entire period shown, which appears to be over 24 hours on the date of 2024-04-25. The rate remained at 0 for the entire period. Therefore, we can conclude that there was no change in the exchange rate throughout this period.
2. Identifying any seasonality or recurring patterns in the changes of exchange rates
Since the exchange rates remained constant throughout the entire day, there's no evidence of seasonality or recurring patterns in this dataset. The MRO rate doesn't seem to be influenced by the time of day, as it stayed the same for each given timestamp. This could suggest that, for this particular day, the MRO was not subject to any intra-day fluctuations.
3. Noting any outliers, or instances where the exchange rate differs significantly from what would be expected based on the trend or seasonality
As the exchange rates did not vary at all throughout the dataset, there are no outliers. All the recorded rates are consistent with each other, which is not typical for exchange rates as they are usually subject to fluctuations. However, in this specific dataset, there were no fluctuations or abnormal values identified.
2024-04-24
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Overall Trend Analysis
It seems there is no noticeable change in the trend because the MRO column consistently carries a value of 0 throughout the dataset. This indicates that the exchange rates have remained stable throughout the given time period, showing neither increase nor decrease.
Seasonality and Recurring Patterns Evaluation
In this particular dataset, no seasonality or recurring patterns have been noted. The MRO exchange rate appears to be consistent throughout the timestamps provided, which suggests a certain degree of stability. Although the data is time-series, the lack of variation in exchange rates makes it challenging to identify any cycles or recurring patterns.
Outliers Identification
Given the constant stability of the MRO exchange rate throughout the recorded timestamps, there are no identifiable outliers in this dataset. Typically, outliers would appear as unusually high or low values compared to the general trend in exchange rates. However, in this particular dataset, all MRO values are consistent, which implies there are no outliers.
2024-04-23
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 Financial Dataset
Before commencing the analysis, it should be noted that the data provided for each timestamp is zero. This implies that no changes have been made to the MRO (Exchange Rate) during the period of observation. Hence, the provided dataset does not manifest any changes or deviations in the exchange rate, thus rendering the analysis to a limited extent due to the absence of any quantitative data changes.
Understanding the Overall Trend
Given the dataset, the exchange rate (MRO) remains constant at zero for the entirety of the recorded time. Thus, for the observed period, we can presume that the MRO neither increases nor decreases. There is a total absence of fluctuation, implying a flat or horizontal trend of exchange rates.
Identifying Seasonality or Recurring Patterns
In the context of seasonality and recurring patterns, the constant rate across all recorded times suggests that there is no seasonality present. Since the MRO remains at zero at all times, it cannot be concluded if there are any regular intervals or seasons at which the rate increases or decreases.
Noting Any Outliers
With respect to outliers that deviate significantly from the general trend, it appears, based on the data presented, that there are none. The absence of deviation or variability in the dataset (given that MRO is zero throughout), leaves no room for the existence of outliers. There are no instances where the exchange rate is drastically different from its usual value (which is zero).
Conclusion
Given the absence of variability in the dataset, it is quite challenging to make any decisive conclusions about the overall trend, seasonality, and potential outliers solely based on the data provided. Nonetheless, the constant MRO value of zero points towards a stagnant exchange rate throughout the analyzed period.
It would be helpful to have additional data with non-zero exchange rates to further evaluate and understand the pattern of changes in the MRO accurately.
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: