Latvian Lats Forecast

Summary of Yesterday

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Statistical Measures

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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

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

Statistical Measures

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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

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Statistical Measures

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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

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Statistical Measures

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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.

Summary of Last Week

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Trend

1. Understanding Overall Trend

From a high-level observation, the exchange rate data show a somewhat fluctuating pattern with both increasing and decreasing trends within the given period. The value at the earliest date is 2.24222 and it gradually increases over time to reach a peak of 2.28623. However, after reaching this peak, it shows a general downward trend until it stabilizes towards the most recent data points. The overall direction of the trend suggests a modest increase in the long term.

2. Identifying Seasonality

Within the given data, it is challenging to definitively identify any seasonality or recurring patterns just based on the raw numbers. This is because the data is limited and does not cover a sufficient length of time (e.g. multiple years) to establish clear seasonal trends. However, if we partition the data to smaller time frames, some repeated patterns might become apparent. For instance, there might be intra-day patterns that appear when the market is open. Nevertheless, without further information or context, making definitive claims on seasonality is difficult.

3. Noting Outliers

Identifying outliers within this data set can be difficult without applying statistical analysis. One way to identify potential outliers would be by looking for the highest and lowest points and their deviation from the general trend. For example, on 2024-04-10, the exchange rate jumps to 2.2563, which seems to be a significant increase from the previous data point. On another occasion, the exchange rate falls to 2.22909 on 2024-04-04, which also is a significant deviation from its previous value. These could be potential outliers, but further statistical analysis would be required to definitively categorise these instances as outliers.

Summary of Yesterday

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• Difference of Daily High & Low:

Statistical Measures

• Mean:
• Standard Deviation:

Trend

Overall Trend

After analyzing the given set of data, it's evident that the exchange rates are fluctuating up and down across the time interval. However, there seems to be a general slight upward trend in the exchange rates from 2.27265 to 2.28596, indicating a minor strengthening of this particular currency during this period.

Seasonality

Determining any seasonality or recurring patterns in the exchange rate changes from the given dataset is challenging without having more data that covers full seasonal cycles (e.g., yearly, quarterly). However, we can observe from the current dataset that daily rates exhibit some fluctuations within minor ranges and then continue their main trend. Therefore, there might be a daily pattern in this, which could be attributed to the daily trading cycle (opening and closing of different financial markets around the globe) affecting the currency exchange. Yet, to confirm these insights, a more granular (hourly or minute based) or longer dataset would be required.

Outliers

In terms of outliers or significant deviations from the expected exchange rates based on the apparent trend or supposed seasonality, the dataset doesn't show any significant outliers. The exchange rates mostly fluctuate within a narrow range and follow an apparent general trend. However, there are a few instances where the exchange rates show a bit of volatility, but without knowing the exact nature of the currency and its market dynamics, it's hard to term them as ‘outliers’.

It is important to note that a robust outlier detection would require a more comprehensive statistical analysis, considering metrics such as standard deviation and the interquartile range.

Summary of Yesterday

• Opening:
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• 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

Our data ranges from a minimum of 2.26918 to a maximum of 2.27921. These values suggest that the exchange rate within this time frame is relatively stable, without dramatic increases or decreases. That said, the decrease in the value from the beginning to the end of the period is apparent - starting at around 2.27788 and ending closer to 2.27344. It signifies a slight but gradual depreciation of the exchange rate over time.

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

Given the data sample from a single day, it is impossible to accurately determine any seasonality or recurring patterns from this limited dataset. However, a few patterns can be noticed within this somewhat regular financial fluctuation throughout the day. Specifically, there seems to be a gradual decline in the exchange rate from beginning to end. We presume this might be due to regular daily trading activities, but to identify real, credible seasonality patterns or cyclic changes, a longer period data might be necessary.

Identifying outliers

Most of the exchange rates revolve around the 2.275 mark, with just a few occurrences of values exceeding 2.279 or dropping below 2.27. These could be interpreted as potential outliers, depending on the specific threshold set for identifying outliers. It's important to note that the existence of outliers in financial time-series data is not uncommon and often reflects sudden responses to short-term events or potential data recording anomalies. Therefore, an extended examination is needed to pinpoint and understand the drivers behind these outliers.

In conclusion, whilst there are very slight increases and decreases in the exchange rate within the data, it is generally stable, without abrupt changes. However, please remember this analysis only covers this specific time series. To gain a deeper understanding of long-term trends, seasonality, or anomalies, a wider dataset over a longer time period is necessary.