Latvian Lats 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 Trend

The given data spans from 2024-04-22 to 2024-04-26, a period of around 4 days. From an initial value of 2.26945, the exchange rates noticeably fluctuate, with slight appreciations and depreciations. However, when analyzing the opening and closing values of the dataset, the closing value on 2024-04-26 (2.25756) is lower than the opening value on 2024-04-22 (2.26945). Thus, it can be concluded that the overall trend in exchange rates for this period is a decrease.

Seasonality and Recurring Patterns

While time-series data sometimes exhibit clear seasonal trends, this dataset might not be long enough for a meaningful seasonal analysis. There's also no obvious recurring pattern discernible from the data in this short a time-frame.

Outliers

Most of the observed exchange rates cluster in the range of approximately 2.258 to 2.270, with few significant deviations. However, one possible outlier could be the rate of 2.26172 at 2024-04-24 04:00:02, and the rate of 2.26388 at 2024-04-24 23:00:02, which are relatively high compare to nearby data points. As mentioned above, without any additional information or a longer time series, it is hard to make a definitive statement about these data points.

Please note that this analysis purely relies on the provided dataset, and no external factors such as market opening/closing hours, weekends/holidays, or the release of key financial news and reports were considered.

Summary of Yesterday

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

Statistical Measures

• Mean:
• Standard Deviation:

Trend

Given the scope of the request and the frequency of data, a comprehensive analysis is as follows:

1. Understanding the overall trend of the exchange rates.

The dataset starts with the level slightly above 2.25708 and ends at around 2.25916. Given the minor difference, it's safe to say that the exchange rate shows a nearly flat trend in the given timeframe. That said, there are fluctuations within this period, and the rate does not remain exactly stable.

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

Given the limited timeframe (less than a day), it's challenging to detect true seasonality. However, some recurring patterns can still be noticed if we focus on changes within the day. It appears that there are several peaks and troughs throughout the day, indicating some form of intra-day variability.

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

• The exchange rate at 2.26376 (at 10:15:02) marks the highest value in the dataset, visibly standing out from the rest.
• Shortly after, we have the lowest point in the dataset at 2.25756 (at 14:00:01).

Whilst the rest of the data does not fluctuate far from the mean, these two points represent significant outliers which deviate from the established pattern.

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:

In this data set spanning from 2024-04-25 00:00:02 to 2024-04-25 23:55:02, the exchange rate shows a slight overall decrease. Starting at a value of 2.26336, the exchange rate fluctuates over time but a general downward slope can be discerned concluding at a final value of 2.25724, which is lesser than the starting rate.

Seasonality or Recurring Patterns in Exchange Rates:

On an intra-day level, it is difficult to identify precise seasonality or recurring patterns in exchange rate changes from this data. The rates fluxuates throughout, with periods of slight increase followed by decreases. However, considering entire day of data, certain periods of stability and increased volatility can be seen. The presence of more data points or observations would be beneficial to better observe potential patterns across different times or days.

Outliers in the Data Set:

From the given data, a few timestamps show significant change in the currency's exchange rate, differing from the numbers before and after. These instances can be considered as the outliers in this dataset, marked by a relatively significant increase or decrease in the rates. For example, the rate spiked to 2.26925 at 2024-04-25 08:15:02, which is significantly higher than the rates around that time. However, these fluctuation are not extremely dramatic, therefore we can say that there are no major outliers in the dataset. It is advisable to study these cases in real-time context as they might indicate critical market events or news announcements.

Please note, however, that the assessment above is based on a general inspection and interpretation of the data. A more rigorous statistical analysis may yield additional insights and more precise identification of trends, seasonality, and outliers.

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

After performing a comprehensive analysis on the provided dataset, it became clear that the overall trend of the exchange rates throughout the day presents minor fluctuations. There isn't a consistent upward or downward motion, rather the rates oscillate within a certain range. This shows that during this particular day, the exchange rate remained relatively stable.

Seasonality and Recurring Patterns

While evaluating this time series data for seasonality or recurring patterns, it appears that this dataset isn't large enough to properly indicate any definitive seasonal changes or recurring patterns. More data spanning over a more extended period (preferably covering a few months or a year) would be required to appropriately identify any such trends.

Identifying Outliers

As far as outliers are concerned, none were immediately apparent within the given data, keeping in mind the exchange rates fluctuated within a reasonably tight range. It thus appears that, during the confusion of this particular day, there weren't any significant events that led to any drastic swings in the rates. The lack of abrupt or sharp changes in rates often indicates marketplace stability and a lack of impactful news during the period.

Please note: It is important to consider that exchange trends and outlier identification can significantly change with comprehensive datasets over extended periods of time.

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 the Overall Trend

The exchange rates in this dataset display a fairly stable trend with slight fluctuations. The first datum starts at a value of 2.26463, and the final datapoint sits at 2.25912, showing a very small decline over the timeframe. While there are intermittent increases and decreases in the rates, the values largely oscillate in the range of 2.26 to 2.27, indicating a generally stable pattern.

Insight on Seasonality or Recurring Patterns

The dataset does not provide clear evidence of strong seasonality or recurring patterns. There is no discernible pattern based on the hour of the day, nor is there a visible weekly pattern based on the day of the week (although this data only covers a single day so this is harder to determine). The minor variations that do occur do not follow a visible recurring pattern, suggesting that they may be random or based on factors not included in this dataset.

Notations on Outliers

No major outliers are visible in the provided dataset. All values are within a narrowly defined range from 2.25 to almost 2.27, they show no large or abrupt changes in the exchange rates that could be considered outliers. The lack of outliers in the dataset indicates a certain level of stability in the exchange rates.

Consideration Factors

• The analysis does not consider specific events or external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports, which can have significant impacts on the exchange rates. This might affect the ability to perceive existing patterns.
• The provided data covers only one day, which could limit the depth of the analysis, as longer-term patterns, trends, and seasonal variations might not be visible in such a short period.
• No forecast has been generated for future rates as per the request.

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

Overall Trend of Exchange Rates

The respective timestamp and LVL currency values show a somewhat undulating and volatile trend. The data starts at a point of 2.2695, and it seems to have fluctuations going up and down over time, ending at 2.26462. Thus, the overall trend does not show a definitive and clear increase or decrease. It's more of a volatile stability, with many fluctuations.

Seasonality or Recurring Patterns

As for seasonality or recurring patterns, these are usually spotted on a longer timeframe, ideally yearly or quarterly. However, this data appears to relate to a daily timeframe, making the identification of usual seasonality patterns difficult. However, general financial knowledge would suggest increased volatility during the opening hours of markets, though this isn't explicitly noticeable in this dataset.

Identification of Outliers

Identifying of outliers form this textual data is quite complex, it would require a conversion of this data into a graphical plot to spot any significant deviations visually. However, looking through the provided numbers, there does not seem to be any drastic shifts within the numbers to suggest strong outliers. The data seems to be within a close range of values.

Note: This analysis is a basic summary of the data trends. A more comprehensive analysis might require statistical methodologies like decomposing the time series into trend, seasonality and residuals, creating moving averages, or applying time-series specific models like ARIMA or Holt-Winters method. Furthermore, for a more thorough understanding, a visualization of the data is highly recommended.