Seychelles Rupee Forecast

Summary of Yesterday

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Statistical Measures

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Trend

1. Understanding the Overall Trend

Based on the provided dataset, the exchange rate of SCR has not remained constant but rather has shown both increases and decreases during the period in question. The rate started at 0.10137 then seemingly experienced a slight drop and subsequently a recovery. For the majority of times, the rates remained around 0.101. However, there were periods where the rate dropped significantly to around 0.095-0.099. The provided data concludes with a rate of 0.09413. This suggests that overall, there might be a slight decreasing trend.

2. Identifying Seasonality or Recurring Patterns

Within this data set, it is difficult to identify definitive seasonality or recurring patterns due to the high frequency of the collected data (every few minutes in a single day) and the somewhat minute changes in rates. The data points do show minute fluctuations within a tight range, but there are not clear patterns of rise and fall that would indicate daily seasonality within this single day's worth of data.

3. Noting any Outliers

There appear to be a few outlier points within the dataset where the exchange rate experience sudden significant drops. Notably the values around 0.095 and also around 0.092. These could possibly be due to an event or occurrence not specified within the given data. Understanding the reasoning behind these outliers would require more contextual information or additional data from surrounding days to place it as part of a trend.

In summary, from this particular dataset, the exchange rate of SCR has shown small fluctuations with a slight overall downtrend. However, caution should be applied in drawing conclusion from this analysis, as the data covers only a short time period and may not reflect the longer-term behavior of the SCR exchange rates.

Summary of Yesterday

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Statistical Measures

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Trend

Analysis of Overall Trend

By studying the dataset, we can find that the overall trend for the exchange rates does not exhibit a clear continuous upward or downward direction. The rate began at 0.1009, increased slightly, then decreased noticeably to around 0.09548. It then gradually rose, with fluctuations, reaching the highest reported value of 0.10138 towards the end of the observed period. However, the net change from beginning to the end is quite minimal, implying a general stability in the exchange rate.

Identification of Seasonal Patterns

Given the frequency and range of the data, it's challenging to identify clear seasonal trends or recurring patterns. While there are cyclical variations throughout the dataset, these are not consistent with a fixed time frame (day, week, etc) which could be indicative of defined seasonality. More granular data (especially hourly or minute data) may be necessary to identify intraday patterns, if they exist.

Observation of Outliers

There are several noticeable outliers in the dataset where the exchange rate undergoes abrupt and significant changes. For instance, the observations at timestamps '2024-04-24 00:40:02', and '2024-04-24 02:35:02' are examples of such outliers.

These inconsistencies might be due to a variety of reasons. Large transactions, flash crashes, manual data entry errors, or changes in broader financial market conditions can all contribute to such abnormalities. It's important to review these outlier values and investigate their causes for a comprehensive understanding of the exchange rate dynamics.

In conclusion, the dataset presents a generally stable trend in exchange rates with occasional fluctuations and apparent outliers. Without a clear seasonal or directional trend, any predictive analysis would require a more sophisticated approach beyond the scope of this initial review.

Summary of Yesterday

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Statistical Measures

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Trend

Overall Trend of Exchange Rates

From a cursory view and without performing any statistical analysis, the general rate seems to be fairly stable, fluctuating around 0.10074 to 0.10090. There are sporadic instances where the rate dips or spikes beyond these boundaries, but the trend generally remains largely stable within this slight range.

Identification of Patterns

Some patterns can be observed within the timestamps given. A closer look into specific timings and intervals might result in some form of seasonality but based on the data provided, no distinct seasonal patterns can be inferred at this point.

Outliers

There are instances where the exchange rate seems to dip below the usual minimum threshold of around 0.10074 such as on timestamps like 2024-04-23 05:50:02 with rates like 0.0997. Also, other instances where it spikes beyond the usual maximum threshold of around 0.10090 can be observed. Rates like these might be considered as outliers based on the general trend of the data.

External Factors

You have stated that we won't consider any external factors for this particular analysis such as market opening/closing hours, weekends/holidays, or the release of key financial news and reports which are integral in analyzing financial data sets. Therefore, this analysis is based on the assumption that the SCR exchange rate is influenced solely by market forces of supply and demand.

No Forecast

No future forecast will be generated in accordance to your stipulations. This analysis is purely based on the internal patterns, trends and outliers of the SCR exchange rates within the timestamps provided.

Conclusion

The SCR exchange rates seem generally stable within a limited range. No specific seasonal patterns can be inferred based on the data. A handful of outliers exist where the rates dip or spike beyond the usual thresholds. This analysis is conducted based solely on the internal factors given in the data set and no forecast for future rates shall be provided.

Summary of Last Month

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Statistical Measures

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Trend

Understanding the Overall Trend

The first step in analyzing the provided time-series data on exchange rate movements is to understand the overall trend. After careful examination of the dataset, it appears that the exchange rates demonstrate a fluctuating trend from 0.0954 to 0.10072 over the given period. However, it is essential to note such movements may bear various interpretations depending on the broader market context and prior rate trajectories.

Identifying Seasonality or Recurring Patterns

To detect any seasonality or recurring patterns within the data, we delve into the timestamps and corresponding exchange rates. Unfortunately, the provided data scope is limited to a single day, remarkably constraining our ability to observe any significant recurring trends or patterns that may emerge over longer periods, such as weeks, months, or years.

Noting Outliers

An essential part of data analysis includes identifying outliers or instances where the data significantly deviates from the expected trend. Upon closer inspection, we observed a drastic spike in the exchange rate from 0.09916 to 0.093 at the timestamp of 2024-04-22 06:20:02 to 06:25:02. This change may be considered an outlier due to the high degree of variation from the preceding figures.

However, please note that the determination of outliers largely rests upon the broader market context, including the historical volatility of this particular exchange rate. If large jumps like this one occur quite frequently, they may not be classified as outliers, but simply as elements of a highly volatile market.

In conclusion, while this basic analysis reveals some crucial characteristics of the data, I would recommend a more comprehensive analysis covering a more extended historical period to better capture the market dynamics and behavior of exchange rates.

Summary of Last Week

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Statistical Measures

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Trend

1. Understanding the overall trend of the exchange rates

Based on the given dataset, it appears that the overall exchange rate for SCR fluctuates within a generally narrow band. There doesn’t seem to be a strong upward or downward trend, indicating that the exchange rate is relatively stable over the provided period. There is a slight dip observed on the 16th April 2024 which continues till 19th April 2024. However, there are also instances of minor increases, but these appear to be largely offset by corresponding decreases.

2. Identifying any seasonality or recurring patterns in the changes of exchange rates

In terms of seasonality or recurring patterns, the dataset does not exhibit strong cyclic behaviour. There seems to be some fluctuation in the exchange rates; however, these occur irregularly and do not form a clearly identifiable pattern. Thus, it is difficult to conclusively identify any seasonality based on the provided data.

3. Noting any outliers, or instances where the exchange rate differs significantly from what would be expected based on the trend or seasonality

There are certain points in the data where the exchange rates noticeably differ from its usual range. An instance of this is on the 16th to 19th of April 2024, where the exchange rate dropped from around 0.102 to less than 0.097. This represents a significant deviation from the rates observed before and after this period. However, without external context or additional data points, it’s difficult to understand the cause of these outliers, especially since the analysis does not take into account external events or conditions.

Summary of Yesterday

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Statistical Measures

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• Standard Deviation:

Trend

1. Overall Trend Analysis

After initial analysis of the time-series data related to exchange rates, an overall upward trend is observed over the period from April 15, 2024 to April 19, 2024. The series starts with the exchange rate at 0.09821 on April 15, 2024, and ends with the rate at 0.10112 on April 19, 2024. It is important to note that financial data such as exchange rates can have high volatility, therefore, slight fluctuation during the period is normal. There were two noticeable dips in the exchange rate during this period, one on April 16 and one on April 18.

2. Seasonality and Recurring Patterns

In the given dataset, it is difficult to gauge seasonality as the data is only available for a brief period of five days. Seasonality analysis would usually require data for an entire year or multiple years for more reliable insights. However, a recurrent pattern can be surmised from the available data where the exchange rate seems to peak more often during certain time windows (eg. around 10:00,15:00 and 22:00) and experience a drop (eg. around 07:00 and 17:00). This can be indicative of the opening and closing of major global financial markets; yet as per your direction, we are not considering external factors.

3. Outlier Identification

While analyzing the given time-series data, a few potential outliers were identified where the exchange rate differed significantly from its immediate past rates. Specifically, prominent dips around 17:00 on April 16 and April 18 may be considered as outliers. Nevertheless, these changes do not appear to be extreme, given the inherent volatility of financial markets. Further statistical analysis would be needed to formally classify these as outliers.

Summary of Yesterday

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Statistical Measures

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Trend

Overall trend of Exchange Rates

The given dataset for SCR exchange rates shows that there is a very slight increase in the rates over the period from 2024-04-19 00:00:02 to 2024-04-19 14:55:01. The rate starts at 0.09612 and eventually reaches 0.10119. This shows a gradual increase in the rates during this period.

Seasonality or Recurring Patterns

The trendline suggests that the variation in these rates doesn't follow a particular pattern or seasonality. The time series plot shows a relatively steady line with small peaks and troughs suggesting that the variances do not repeat at specific, identifiable intervals and are more likely due to random changes in the data. Moreover, there is no constant, typical period running through the entire time period that suggests any seasonality.

Outliers or Significant Deviations

There are no glaring outliers in the data set, which indicates that there is relatively less risk of extreme values distorting the analysis. This assessment is made based on the values' closeness to the progressive mean, where no readings show a significant deviation. There are few points where a slight spike in the exchange rate is observed (such as around "2024-04-19 07:00:03" and "2024-04-19 08:00:03"), but the rate seems to quickly stabilize again, indicating non-persistent shocks.