Seychelles Rupee Forecast

Not for Invesment, Informational Purposes Only

Summary of Yesterday

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Statistical Measures

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    Overall Trend of the Exchange Rates

    Based on the analysis of the provided data, there are noticeable fluctuations in the SCR exchange rate throughout the day. At the start (00:00:02), the rate is 0.09413 and at the end of the day (23:55:02), the rate dropped a bit to 0.09993. Thus, it can be said that there has been a slight decrease in the rate in the given period. The number fluctuates up and down several times during the day, but overall it shows a decreasing trend.

    Seasonality or Recurring Patterns in Exchange Rates

    Upon the initial assessment of the dataset, no particular seasonality or recurring patterns are apparent in these exchange rates. Exchange rate data of one day span cannot be used to determine or predict seasonal trends as it is too short. More data across several months or years would be needed to unlock insights about seasonal fluctuations and identifiable patterns. However, please note that foreign exchange market movements can be highly random and influenced by numerous unforeseen variables.

    Outliers Observed in the dataset

    Several potential outliers exist in the dataset provided. An example is the jump in the exchange rate from 0.09413 at the start of the day to 0.10041 at the 02:15:01 timestamp, indicating a substantial increase in value. This could potentially indicate an event or anomaly during this time period. There is also a noticeable decrease in the exchange rate from 0.10044 at 06:20:01 to 0.09976 at 09:20:03. These peaks and troughs likely indicate volatile periods for the exchange rate within the studied day. The nature and details of these potential outliers would require additional investigation.

Summary of Yesterday

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Statistical Measures

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    Overall Trend Analysis

    Upon analyzing the dataset, it's clear that the exchange rates (SCR) do not show a linear development within the period provided. They fluctuate significantly across different timestamps. However, we can define three main periods differentiated by their average SCR level: the initial period from 2024-02-28 00:00:02 until approximately 2024-02-28 11:30:04, where the SCR values mainly oscillate around 0.101. The second period from 2024-02-28 11:30:04 to around 2024-02-28 22:55:02 is characterized by lower SCR values around 0.100. The third is a short period at the end of the dataset, from 2024-02-28 22:55:02, where the value decreases dramatically and stays around 0.094. We can therefore note a more general decreasing trend in these exchange rates across the day.

    Seasonality and Recurring Patterns Identification

    Regarding seasonality and recurring patterns, the provided data represents a short time frame within a single day. Different rates throughout the day might call attention to the influence of forex market opening hours. Still, given the data covers only a day, it's challenging to identify a clear and strong seasonality or repetitive patterns without additional historical data.

    Outliers Analysis

    Resulting from the analysis, a crucial outlier can be recognized at 2024-02-28 22:55:02, where the rate drops from 0.1003 to 0.0941. This point significantly differs from the general trend followed over the day and represents the lowest value in the dataset. No gradual drop leads to this value, and there’s no resurgence afterward, which makes it a considerable outlier within this dataset.

    In conclusion, while the dataset suggests a general decreasing trend in exchange rates throughout the day, identifying clear, repetitive patterns or seasonality would require a longer timeframe. The dramatic drop noted near the end of the day is an unexpected outlier and might be due to specific, unknown factors.

Summary of Yesterday

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Statistical Measures

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Summary of Last Month

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    1. Understanding the Overall Trend

    Based on the data provided, there appears to be a minor fluctuation in the exchange rate over the given period. It seems to start at around 0.10061 at the initial timestamp, drops to 0.09501 and then gradually recovers back to 0.09888 by the end of the period. What this indicates is that over the course of the day, the exchange rate decreased primarily, with a slight recovery near the end.

    2. Identifying Seasonality or Recurring Patterns

    Given the timespan provided in the data, based on a single day's data, it's tough to determine whether seasonality or recurring patterns are present. However, by inspecting the data, it appears that there are small oscillations in value, with the exchange rate rising and falling slightly within short intervals. These minute fluctuations could be a result of normal market volatility or other short-term influences on the exchange rate. However, to make a definitive statement on seasonality, a larger dataset spanning over longer period of time (ideally covering a whole year) would be needed.

    3. Noting Any Outliers

    Without a statistical analysis, it’s hard to quantitively identify any outliers. However, visually inspecting the data, the lowest value 0.09501 seems to be significantly lower than the remaining values which may be considered an outlier.

    Keep in mind, this analysis is very high level. For a more detailed understanding, and to confirm these observations, a detailed statistical analysis should be performed which includes creating visual plots, calculating summary statistics, and applying tests for trends and outliers.

Summary of Last Week

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Statistical Measures

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    Understanding the overall trend of exchange rates

    Analyzing the given data, it is noticeable that the exchange rates fluctuate over time. There are points in the dataset where the rate increases, decreases, and also remains relatively stable. However, looking at the broader picture, it seems that there's a volatile trend in the exchange rates without a distinct pattern of consistently increasing or decreasing over the presented period.

    Identifying any seasonality or recurring patterns

    To identify seasonality or recurring patterns, it requires more consistent and longer time data, like daily data for at least a few years. The given dataset captures the fluctuations of exchange rates over days to weeks, and likely does not cover a full seasonal cycle to analyze such a pattern. Thus, no discernable recurring or seasonal patterns can be identified from the given data. The exchange rate changes seem to be more random than cyclical according to the timestamp provided.

    Outliers in the exchange rates

    Outliers in this context would constitute unexpected and substantial rises or drops in the exchange rate. As per the given dataset, there are a few instances where the rate dropped or climbed abruptly. For instance, on 2024-01-26, the exchange rate dropped from around 0.10156 to 0.09521, only to bounce back on the next recorded timestamp to 0.09907. Similarly, we can see a drop from 0.10211 on 2024-01-30 to 0.10116 on the next timestamp. Periods like these may be considered as outliers. However, without further data and economic dynamics, it is challenging to specify the reason or categorize them as pure anomalies.

Summary of Yesterday

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Statistical Measures

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    Overall Trend Analysis

    The raw data provided contains the timestamp and corresponding exchange rates in SCR. To understand the overall trend, we look at the first(0.0935) and the last(0.09976) data points and notice an increase. Therefore, from the data provided, it can be inferred that, overall, the exchange rates have increased slightly during the examined period. However, due to fluctuations in rate over time, a more in-depth trend analysis with comprehensive time-series techniques would provide a deeper understanding.

    Identifying Seasonality or Recurring Patterns

    In time-series datasets like this, seasonality would refer to predictable and recurring trends and patterns over a particular period within the dataset. It is difficult to discern any recurring or seasonal patterns from the raw data presented. An examination of the frequency of the exchange rates over different periods could potentially reveal seasonal trends. More sophisticated time-series analysis, for example, decomposing the data into 'seasonal' and 'trend' components, might be necessary for a better understanding.

    Detecting Outliers

    An outlier in the data describes a data point that is significantly different from other observations. An outlier could indicate a possibility of variability in the data or an experimental error. A cursory look at the dataset shows a couple instances where the exchange rate jumps downwards or upwards before returning to its following trend, like the point (2024-02-20 02:00:03,0.09372). However, without further context or more data points, it cannot be affirmed whether these are true outliers or just part of the normal variability in exchange rates. Finally, to accurately detect outliers, statistical tools such as the Box-plot, Z-Score, or the IQR methods could be employed.

    Note

    This initial analysis is based on a simple review of the data provided, and it is recommended to use statistical analysis tools for a deep understanding. It neither considered the external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports nor generated any forecast for future rates.

Summary of Yesterday

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Statistical Measures

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    Data Analysis

    To analyze this time series dataset about SCR exchange rates, we will identify the overall trend, check for seasonality or recurring patterns, and note any noticeable outliers.

    Overall Trend

    Our dataset starts with an exchange rate of 0.10091 on 2024-02-23 00:00:02 and ends with a rate of 0.09899 on 2024-02-23 14:55:01. The most significant drop in the exchange rate happened around 2024-02-23 10:35:03, where the exchange rate fell dramatically to 0.09445.

    In general, the SCR exchange rate over 24 hours fluctuated around an approximate average of around 0.100. There's a clear drop in exchange rate around the 10:30 mark, which could possibly be a temporal trend or part of a periodic regular fluctuation.

    Seasonality and Recurring Patterns

    Due to the limited time period (just over one day) covered in the dataset, it's hard to definitively identify any long-term seasonality or recurring macro patterns. However, micro patterns within the day can be observed.

    Specifically, the rate seems to moderately fluctuate up and down throughout the observed period, with the exchange rate often appearing to ‘recover’ after a decrease. This could suggest that the exchange rate has some degree of autocorrelation, i.e., its current value is influenced by its recent past values.

    Outliers

    The most noticeable outlier within the provided dataset is the aforementioned significant decrease to 0.09445 at 2024-02-23 10:35:03. This was the lowest rate within the entire observed day and was immediately followed by a slight increase, hinting at the possibility of an abrupt market event or anomaly at that point in time.

    However, the dataset provided does not extend enough to comment on whether the 0.09445 value significantly deviates from what would be expected based on longer-term trends or seasonal patterns.

    Remember, this analysis is based on statistical examination of the given numerical series and does not consider external factors that might have affected the exchange rates or should be considered for future forecasting.