Surinam Dollar Forecast

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

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 Trends in Exchange Rates

From a quick overview, there is apparent variation in the exchange rates over the period shown. The exchange rates appear to be relatively stable with slight fluctuations: the value tends to oscillate between around 0.03966 and 0.04029. It is worth noting a considerable increase on April 25th and April 26th, reaching a peak of 0.04029.

Identifying Seasonality

A detailed analysis of seasonality can be challenging to make out from this dataset given the short term nature of the data provided. A more comprehensive dataset which includes observations over an extended period, typically a full year or more, would let us discern seasonal variance more accurately. However, some minor fluctuations, which may be evidence of repeated patterns, would require more investigation regarding their source and reliability.

Noting Outliers

Despite the above-mentioned fluctuations, this dataset doesn't seem to present significant outliers. The majority of values fluctuate within a relatively tight range. The peaks on April 25th and April 26th, with a rate of about 0.04029, could be considered as mild outliers, although their deviation from the mean is not extreme. This is mostly due to the relatively stable nature of the exchange rate for this particular period.

Conclusion

In summary, this dataset is consistent with relatively stable exchange rate behaviour. There is a high degree of stability, with minor fluctuations over time. While it's challenging to discern a specific trend or seasonal pattern from this set of data, the stability in the exchange rate is noteworthy in itself. A broader temporal dataset would be needed to gain insights into seasonality and long-term trends more accurately.

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 Exchange Rate Data Set

Given that the data shared pertains to the change in exchange rate (SRD) over different timestamp intervals, we would evaluate it from three key perspectives, namely: trend, seasonality, and outliers. For the purpose of this exercise, we would not base our evaluations on considerations for market-related or societal factors that could influence these metrics. In other words, our insights would be solely rooted in the dataset provided.

1. Overall Trend of the Exchange Rates

Observe the time-series data reveals a general increase in the exchange rates—beginning with an exchange rate of 0.03992 at the first timestamp (2024-04-26 00:00:02) and peaking at an exchange rate of 0.04032 in the timestamp of 2024-04-26 10:15:02. There after, the exchange rate decreased considerably; having a minimum rate of 0.04021 at the timestamp 2024-04-26 14:00:01. Although there was a slight recovery of the exchange rate (0.04024) as at the last timestamp in the dataset (2024-04-26 14:55:01), the exchange rates generally decreased from 2024-04-26 10:15:02 through to the end of the dataset. In summary, the overall trend indicates initially rising exchange rates till 2024-04-26 10:15:02 (10:15AM) and a subsequent gradual decrease till the end of the dataset.

2. Seasonality or Recurring Patterns

Given the time-series data is over a course of a single day, it's hard to identify a strong seasonality or recurring daily pattern in the exchange rate data without data spanning over multiple days or even months or years. However, it is critical to note that data may be impacted by daily market opening/closing cycles that aren't reflected in this analysis due to its constraints.

3. Outliers in the Exchange Rates

Anomaly detection or identification of outliers requires an analysis of how the exchange rates deviate from an established norm or pattern. Although the current dataset does contain small fluctuations, none of these are significant enough to be termed as outliers. The exchange rate data maintains a relatively stable range of variation throughout the timestamps. However, without an established norm (which usually would require a larger dataset), it's challenging to concretely establish outliers based on the given dataset.

In conclusion, while the analysis reveals a general trend in the exchange rates and does not highlight significant outliers, it is constrained by the lack of sufficient data to infer strong seasonality patterns or define an established norm for outlier detection.

Summary of Yesterday

• Opening:
• Closing:
• Difference of Opening & Closing:
• Daily High:
• Daily Low:
• Difference of Daily High & Low:

Statistical Measures

• Mean:
• Standard Deviation:

Trend

Introduction

The Given dataset is a time-series data which reflects the hourly changes in the SRD currency exchange rates zoomed in on the particular day of 25th April 2024. The data frequency is approximately every 5 minutes, although there are some irregularities. The task is to analyze and identify the patterns, trends, and anomalies in the provided data set.

Overall Trend

The overall trend of the SRD exchange rate for the day appears to be slightly decreasing. The rate starts at 0.03978, then experienced a gradual decrease, falling as low as 0.0397, and closing at 0.03992. The difference between the opening and closing rates may seem minimal but in the currency exchange market, even a small change can be significant.

Seasonality Trends

When it comes to identifying any seasonal or recurring patterns in the changes of exchange rates, it is less straightforward to identify within a single day's data. Usually, this kind of patterns and trends are easier to identify when data spans across several weeks, months or years, so the typical daily, weekly or monthly patterns could be observed. In this case, since we are working with just one day worth of data and the variations in the data are slight, no clear seasonal trend can be identified.

Outliers and Anomalies

Given the tight range of exchange rates over the period (from 0.0397 to 0.04008), it doesn't appear that there are any significant outliers or instances where the exchange rates differs significantly from the general trend. The exchange rate seems to fluctuate within a narrow range for the whole day. Therefore, no clear anomalies can be spotted which could suggest market shocks or irregular trades.

Conclusion

The SRD exchange rates for the given day followed a slightly decreasing trend without prominent seasonal patterns or significant outliers. While the details of each trade remain unknown, the overall pattern suggests a relatively stable and routine trading day.

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 in Exchange Rates

From the time series data provided, the overall trend in exchange rates seems to be showing a slight uptick. The initial SRD exchange rate given at the start of the period was 0.03967, and it appears to increase slowly up to a value of 0.03989 before declining again to 0.03978 towards the end of the period. This suggests a predominantly stable yet fluctuating trend over the sampled period.

Seasonality and Recurring Patterns

Given the granular level at which the data is provided (every five minutes), traditional forms of seasonality (weekly or yearly patterns) are not applicable in this case. The minor fluctuations in data don't clearly indicate any consistent patterns.

Outliers in the Data

At a macro level, there don't appear to be any significant outliers in the data set. Most of the exchange rate values fluctuate between 0.03967 and 0.03989, which is a relatively tight range. However, a more in-depth statistical analysis would be needed to identify if there are any true outliers at a micro level.

Interpreting time series financial data requires careful consideration of the time period in question, the granularity of the data, and the nature of the currency or financial instrument being analyzed. Despite the short duration and granular nature of this data, initial analysis suggests a predominantly stable trend with minor fluctuations, and no significant outliers detected at a macro level.

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

Firstly, looking at the data provided, the overall trend of the exchange rates seems to be fairly stable. The rate varies between 0.03966 and 0.0398 over the duration of the dataset. This indicates a relatively small range, suggesting that the exchange rate has been generally stable over the period of time captured by the data. Sometimes, very slight increase or decrease could be noticed, but mostly it seems to remain consistent.

Seasonality or Recurring Patterns

Regarding seasonality or recurring patterns, given the static nature of the data, it's difficult to identify any significant recurring patterns within this specific dataset. The data does not show any noticeable peaks or troughs that might indicate a seasonal effect or recurring pattern. The potencial micro-fluctuations that might occur in shorter timeframes are not obvious from this dataset.

Outliers and Unexpected Instances

From the data provided there are not any immediate apparent outliers with the exchange rate staying within a very close range of values. It seems that the rates at each timestamp are in line with the overall pattern in the data and do not show any unexpected spikes or dips.

Conclusion

Overall, the exchange rates in this dataset appear to be quite stable over time, with no clear trend of increase or decrease in the values. There do not appear to be any seasonal fluctuations or regular patterns in the data. Similarly, there are no noticeable outliers or unexpected rate changes that could suggest any external influences affecting the rates. Lastly, given the nature of these data, caution must be exercised when using it for future forecasting as it seems that the values are rather constant, and any changes might need to be inferred from other market factors not apparent in this dataset.

Summary of Last Month

• Opening:
• Closing:
• Difference of Opening & Closing:
• Daily High:
• Daily Low:
• Difference of Daily High & Low:

Statistical Measures

• Mean:
• Standard Deviation:

Trend

The data set essentially spreads over one day (April 22, 2024) and represents the exchange rates (SRD) in 5-minute intervals. Below is the in-depth analysis of the provided data set:

Understanding the Overall Trend

The general trend of the exchange rates appears to be relatively stable. The values fluctuate around the 0.03975 - 0.03976 mark. This shows very slight fluctuations, with the minimum value of the exchange rate at 0.03973 and the maximum value at 0.03983 throughout the day.

Seasonality or Recurring Patterns

Given the dataset only covers a single day, identifying seasonality or recurring patterns is challenging. However, there are slight rises and falls throughout the dataset, but no significant recurring patterns could be identified within this single day.

Notable Outliers

There are no standout outliers in this dataset. All fluctuations in the exchange rate remain within a very narrow range, with no notable spikes or drops. The data remains mostly consistent, with exchange rates slowly rising and falling throughout the period.

In conclusion, the major essences of this dataset are the relatively stable exchange rate trend and slight fluctuations around the 0.03975 - 0.03976 range. Identifying weekly or monthly patterns requires data that spans over a more extended period, and with this daily data, it's hard to determine any meaningful seasonality or recurring patterns.