Cayman Islands Dollar Forecast

Not for Invesment, Informational Purposes Only

Summary of Yesterday

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

Statistical Measures

  • Mean:
  • Standard Deviation:
  • Trend

    Understanding the Overall Trend

    Examining the data, we see that over the course of the timestamp provided, the kyd exchange rates have demonstrated small fluctuations. Though there are instances of notable increase and decrease, the exchange rate for the most part remains within the given range which suggests general stability. Overall, there isn't a definitive upward or downward trend as the rates are frequently adjusting back to their mean.

    Identifying Seasonality

    When looking at a possibility of a recurring seasonal pattern within the exchange rates, due to the nature of time-series data, we may anticipate daily patterns where rates may tend to rise or fall depending on the time of the day. However, in this particular dataset, there are no clear indications of a strong daily pattern. This might be due to the brief time steps we have, as usually seasonal patterns are better observed over a longer time frame.

    Outliers

    Understanding the presence of outliers can provide insights into anomalies that deviate from regular trends. Outliers in exchange rates could be due to several factors, including unusual market conditions, surprising economic news, or major geopolitical events. In this dataset, at certain time steps, one may notice some sudden increases and decreases. These abrupt changes might be considered as outliers since they are not consistent with the regular fluctuations in this data series. However, these fluctuations do not seem to cause any drastic shifts in the overall trend, indicating that these could possibly be regular market volatilities.

    In conclusion, while there is no strong trending movement or consistent seasonal pattern in this particular dataset, the small fluctuations and occasional outliers provide us a picture of a generally stable yet frequently adjusting exchange rate.

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. Overall Exchange Rate Trend Analysis

    From the dataset provided, it seems that overall, the exchange rates (KYD) generally show a slight increase over the period provided. Starting at 1.62306 on 2024-02-28 00:00:02, the rate ended at 1.63073 on 2024-02-28 23:55:02, which indicates a steady, although minor, increase. This suggests that the market was leaning towards a more valuable KYD during this time frame.

    2. Seasonality or Recurring Exchange Rate Patterns

    Regarding recurring patterns in the dataset, it is important to note that this type of granular, intraday timescale may not be the best to determine seasonality. Seasonality often requires a longer time frame - often several months to a year - to establish any meaningful trends, especially in financial and economic datasets. With an intraday dataset like the one provided, we are more likely to identify patterns related to market opening/closing hours, which we haven't considered as per your instructions. Thus, from the provided data, a specific recurring pattern isn't immediately visible.

    3. Identifying Notable Outliers

    Anomalies or outliers in the data can occur due to sudden market-moving events. From the data provided, it can be observed that there is a notable spike in the exchange rate at 2024-02-28 06:20:02 when the rate jumped to 1.6301 from 1.62634 at 2024-02-28 06:15:02. This is a significant deviation compared to the incremental changes observed in the dataset and can be deemed as an outlier. Nonetheless, the reason for such a surge cannot be identified without considering external factors, which aren't in the scope of this analysis as per your instructions.

Summary of Yesterday

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

Statistical Measures

  • Mean:
  • Standard Deviation:
  • Trend

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

    Understanding the overall trend of the exchange rates

    Examining the dataset, it is notable that there is a gradual increase in the exchange rate over the given period. The rate starts at approximately 1.61954 and ends at approximately 1.61995 with slight fluctuations in between.

    Although there are small decreases and increases in the exchange rate, the overall trend suggests a relatively steady increase. However, it's worth noting that the fluctuations within this trend can be significant, ranging from around 1.620 to 1.622 at peak points, and dropping as low as around 1.619 to 1.620 at trough points.

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

    Looking at the data, it is not immediately clear if there is a strong sense of seasonality or recurring patterns in this dataset. The exchange rates appear to be determined more by market dynamics such as supply and demand rather than time of day or date. Without additional data or context, it's difficult to identify any clear pattern or seasonal effect. Therefore, more sophisticated analytical methods or more data might be required to uncover any potential patterns.

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

    There are some instances where the exchange rate deviates significantly from the overall trend. For example, the rate peak at about 1.62167 which is quite above the overall trend. Similarly, there are troughs where the value drops to around 1.620 lower than the majority of the data points.

    These outliers suggest that while the general trend is upwards slow increase, there are many moments where significant market events can cause the exchange rate to spike or dip. These could be the result of factors outside the scope of this particular dataset.

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

    Understanding the overall trend of the exchange rates

    The overall trend of the exchange rates over the data provided seems to fluctuate without a clear direction. During a specific day, there are periods where the exchange rate increases, followed by decreases. However, a more comprehensive trend analysis would require a longer dataset covering multiple months or even years. It is important to note that financial data like exchange rates can be influenced by a multitude of factors, both microecononomic - such as interest rates, inflation, and public debt - and macroeconomic - like political stability, performance of other currencies, and overall health of the economy.

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

    Grasping seasonality or recurring patterns in exchange rates can be quite challenging due to the complexity and diversity of the influencing factors. From the data provided, it doesn't seem to show significant seasonality or recurring patterns. In other words, the values don't consistently repeat over a definite period (e.g., every day or week). However, there may exist minute fluctuations within their respective days. Such patterns may be due to regular trading hours, although further analysis would be required to confirm this.

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

    In the dataset provided, notable outliers or drastic changes are not evident. This dataset shows typical fluctuation, which is common in such time-series data. An outlier would typically appear as a sudden and significant deviation from previous data points, either spike or drop. Exchange rate movements are affected by numerous factors, many of which may not be immediately apparent in the dataset we have. Also, these data points might represent market responses to unknown events or changes that were not captured in this dataset.

    It's worth noting that this analysis was carried out without external context like market conditions, political events, or policy changes. These factors can significantly impact the exchange rate and can cause outliers or abrupt changes.

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 of Exchange Rates

    The overall trend of the exchange rates can be observed by looking at the progression of the data from the start to the end of the provided time series. From the provided data, we can see that the exchange rate at the start (02/19/2024 01:00:02) is 1.61707 and the exchange rate at the end (02/23/2024 14:00:01) is 1.61966. This indicates a slight increase in the value of the exchange rates KYD over the specified period. However, one should note that there are fluctuations throughout and the general trend might be influenced by these fluctuations.

    Seasonality and Recurring Patterns

    Regarding seasonality or recurring patterns, it is not obvious from a manual inspection of the data. A more in-depth statistical analysis would be needed to accurately identify any patterns or seasonality in this dataset. In time-series data, some common patterns to look for include annual seasonality (patterns that repeat each year), weekly seasonality (patterns that repeat each week), and trends (continuous increases or decreases over time).

    Outliers

    Outliers in the exchange rates can be identified by looking for instances where the exchange rate is much higher or lower than what is typical for that time period. For example, looking through the provided data, the highest exchange rate is 1.62575 (2024-02-21 03:00:02) and the lowest is 1.61267 (2024-02-23 02:00:02). These could potentially be outliers. However, to establish these as outliers in a comprehensive manner, an in-depth statistical analysis is required, which include techniques like IQR (Interquartile Range), Z-score, and more.

Summary of Yesterday

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

Statistical Measures

  • Mean:
  • Standard Deviation:
  • Trend

    In this time series analysis, we'll analyze the exchange rate data provided. Aggregated observations are converted into various time series to study the trend, seasonality, and outliers that could exist in the raw data.

    1. Understanding the overall trend of the exchange rates

    The data shows a range of variation in the KYD exchange rate from 1.61218 to 1.62147. The overall trend showcases an increasing trend from the beginning of the dataset until the end. The exchange rate started at around 1.61322 and has gradually risen to 1.61984. However, it does fluctuate throughout the period with occasional dips.

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

    Given the short period of the data, it is challenging to spot any obvious seasonality within the numbers, considering the data is intraday, it doesn't seem to showcase a clear pattern. Hourly data is not generally adequate to deduce weekly, monthly, or yearly patterns.

    3. Outliers or significant variations

    There isn't a clear instance of an outlier in the data given. Despite occasional rises and dips, the exchange rate appears to follow a general and steady increasing trend. The fluctuations are representative of the general volatility typical in exchange rates and don't seem to be indicating any extreme or unexpected behavior.

    For a more accurate analysis, other external variables such as volume, volatility index, etc., could have been considered. This analysis is based on the given data ignoring external factors such as market opening/closing hours, weekends/holidays or the emergence of key financial news and reports. I have not developed any forecast for future rates either. I hope you find the time series analysis helpful!