Lithuanian Litas Forecast

Not for Invesment, Informational Purposes Only

Summary of Yesterday

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Statistical Measures

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    Data Analysis

    The dataset provided seems to be a representation of hourly exchange rates of an unknown currency represented as 'LTL' to another unknown currency across a span of one day. The currency pair is not specified but will not necessarily hinder the analysis.

    1. Overall Trend of Exchange Rates

    Upon inspection, the exchange rate begins at around 0.45972 at the start of the day. Thereafter the exchange rate fluctuates in a narrow band of roughly 0.459 to 0.460 for several hours before hitting an approximate peak of 0.46048 in the 07:15 to 07.25 hours bin. After this peak, the exchange rate starts dropping until it hits the lowest value of 0.45866 at 09:10 hours. After this dip, the exchange rate climbs back up to around 0.460 before dropping significantly down to 0.4588 and then gradually rises up and hovers around 0.459 mark towards the end of the day. Hence it can be stipulated that there's a general upward and downward trend observed in the data but no consistent increase or decrease for the entire period under study.

    2. Seasonality and Recurring Patterns

    As the data spans only a single day, it may not be appropriate to comment on the seasonality aspect of the timeseries data. That being said, the data at hand does not demonstrate any readily identifiable patterns or regular fluctuations that repeat over specific time intervals. The fluctuations are appear random with no visible repetitions.

    3. Outliers

    Outliers usually refer to instances where the exchange rate considerably deviates from the general trend or varies heavily compared to its immediate predecessor and successor. The exchange rates in the given data do not seem to show such heavy fluctuation, putting into perspective the very minor difference between the maximum and minimum exchange rates, which implies the lack of probable outliers in this dataset.

    N.B.

    Please note that further and more detailed analysis can be achieved with larger timeseries datasets, which include data spanning over months or years. Additonally, identifying and analysing the impacts of external factors such as market opening/closing hours, weekends/holidays or the release of key financial news and reports, can result in a more nuanced understanding of the exchange rates' behaviour.

Summary of Yesterday

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  • Difference of Daily High & Low:

Statistical Measures

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  • Trend

    Analysis

    Without applying sophisticated statistical models but by just looking and calculating changes, we can see the following trends and patterns from the dataset:

    Overall Trend

    Based on the time series data provided, the overall trend for the exchange rates appears to increase slightly. The rate starts at 0.45883 and ends at 0.45963. This represents a small upward movement over the timeline provided.

    Seasonality and Recurring Patterns

    Detection of seasonality or recurring patterns can be a bit challenging without observing data for a longer period. With the given dataset, it's hard to declare any significant seasonality or recurring patterns in the exchange rate. However, certain intervals with micro up and down fluctuations can be observed which can be attributed to the intra-day trading volatility. To understand the specific seasonality like daily or weekly, we need more data from multiple cycles.

    Outliers

    Analyzing the specific outliers in the data set would require a more detailed statistical analysis which is beyond the scope of your request. However, in a visual observation, there are points where rate deviates from its direct neighbouring points, but without a defined threshold for what we consider an 'outlier', it’s hard to categorise them as such. These are more likely to be random fluctuations inherent in financial data.

    Summary

    In summary, the overall trend of the LTL exchange rate seems to slightly increase over the provided period. It's not clear if there are specific seasonal or recurring patterns from the provided dataset. Additional analysis could potentially reveal intra-day patterns or trading session related patterns. Lastly, while there are deviations point to point, no specific outliers are immediately evident without further in-depth analysis and setting an 'outlier' threshold.

Summary of Yesterday

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Statistical Measures

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  • Trend

Summary of Last Month

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Statistical Measures

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    1. Trend of Exchange Rates

    Upon examining the dataset, it seems that the exchange rate has shown minute changes over time. It started at 0.45755 and ended at 0.45729 on 2024-02-26. The highest rate that day was 0.45817, and the lowest was 0.45722. This suggests a slight decrease in the exchange rate over this time period. The decrease is, however, marginal and could be within the typical daily fluctuation range for a stable currency.

    2. Seasonality and Recurring Patterns

    A more detailed analysis would be required to accurately identify any seasonality patterns or recurring trends in the exchange rates. Given the data subset provided, it is challenging to identify any clear recurring patterns. The exchange rate fluctuates throughout the period but doesn't show a clear and identifiable pattern. This fluctuation could be attributed to normal trading volatility within that specific trading day.

    3. Outliers

    Regarding instances where the exchange rate differs significantly from the trend, the dataset's scope—spanning just one day—does not allow for a robust identification of outliers. However, the data points that reach the extreme high and low (0.45817 and 0.45722, respectively) could represent significant deviations from the overall day's trend. Bear in mind that these aren't necessarily outliers, as these rates could very well be within the currency's usual fluctuation range.

    Please note that this analysis is done in isolation of any external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports.

Summary of Last Week

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Statistical Measures

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    Overall Trend

    The overall trend of the exchange rates over the period can be interpreted as a gentle decrease with majority of the fluctuations being relatively small. The data began at a value of 0.45632 and ended marginally lower at 0.45723 which indicates a very slight overall decline in the exchange rate over the period. The fluctuations between these points however, were mostly within a narrow band which suggests that there weren't any dramatic swings in value.

    Seasonality or Recurring Patterns

    At first glance, the exchange rates appear to fluctuate in a seemingly random manner, making the identification of a definite seasonal trend or recurring pattern difficult. However, very short-term, micro patterns seem to be present between consecutive data points, as the exchange rate appears to undergo a regular rise-and-fall pattern from one data point to the next. This suggests that there is some degree of volatility in the exchange rate on a very short-term basis. But without further data regarding the time of year, day of week, etc., it is hard to determine a definite seasonal pattern.

    Outliers

    Distinct outliers in this dataset are hard to identify at this perspective due to the aforementioned short-term volatility. The exchange rate frequently deviates from its immediate surrounding average, causing many potential 'outliers'. A few possible outliers include a slight dip to 0.45245 and a spike to 0.46008. However, these deviations aren't significant and the fluctuations promptly revert back to the observed average rates. This indicates that while we see volatility, we are not seeing dramatic and lasting deviations from the average rate.

    Overall, this analysis has provided a general interpretation of the patterns and trends of the LTL exchange rate given the provided data. It should be noted that possible interactions with external factors as well as larger scale seasonal patterns could be missed due to lack of such information for this specific dataset.

Summary of Yesterday

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Statistical Measures

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  • Trend

    Understanding the overall trend of the exchange rates.

    Looking at the data provided, it can be seen that the exchange rate began at 0.4563 on 19/02/2024, ending at 0.45723 on 23/02/2024. The clear trend, therefore, over the data period shown, is a slight increase in the value of the exchange rate.

    Although the rate had minor fluctuations during this period; it remained relatively stable. On 20/02/2024, there was a distinct climb to a peak of 0.45826 on 21/02/2024. This climb was the largest fluctuation in the data provided and established the incremental trend visible in the exchange rate.

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

    As the provided data is restricted to only a few days, it is challenging to definitively identify any seasonal patterns or well-defined recurrent changes in the exchange rates. However, a cursory overview of the data seems to suggest daily fluctuations are present. The rates appear to increase to a certain point within the day and then dip, before the pattern repeats. Protracted analysis over a longer time period is necessary for substantiating these initial observations, though.

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

    There were not any significant outliers observable in the data. As previously stated, the exchange rate maintained a stable level, with no unexpected leaps or falls in value. The most substantial fluctuation in rate occurred on 20/02/2024, reaching its maximum on 21/02/2024. Still, this could be expected within such time-series financial data and does not seem anomalous within the data's overall context.

    Analysing such data for a more extended period would offer a clearer understanding of the common fluctuations and establish whether those observed in this set are regular or anomalies.

Summary of Yesterday

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Statistical Measures

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  • Trend

    Overall Trend of Exchange Rates

    By observing the given dataset on timestamp and ltl exchange rates, the overall trend for lev exchange rates seems relatively stable with a slight inclination towards an increase. The rate started with a value of 0.45656 and ended at 0.45728. This is a slight increase, which could potentially point to a gradual yet consistent rate increase. Keep in mind; this is just a preliminary analysis without considering the fundamental shifts that might have occurred during this time frame. An in-depth analysis would necessitate an examination of the dataset under daily, weekly, or monthly granularity.

    Seasonality and Recurring Patterns

    From the given data, it's difficult to discern clear seasonality or recurring patterns due to the limited span of time we have data for. This might require collecting data over longer periods to observe. Seasonal patterns often emerge in a broader timeframe, for instance, detecting hourly patterns would require at least a few days of data, for daily patterns a few months, and for weekly or monthly patterns a few years of data would be required.

    Outliers in exchange rates

    There do not appear to be any significant outliers, or instances where the exchange rate differs remarkably from the established trend in the provided dataset. Although small fluctuations are noticeable, they are normal in a typical market situation. This could simply be daily volatility or noise, but it may not reflect a significant event or disturbance. Outliers in financial data often signify important events that dramatically affected the market, and a comprehensive analysis of the market situation would be required to explain them.

    Summary

    • The overall trend appears to be quite stable, with a tread towards a slight increase.
    • The data provided does not enable the recognition of any clear patterns or seasonality.
    • There are no significant outliers evident within the dataset.

    Please note this is a basic analysis of the presented dataset. A more comprehensive analysis that would include statistical modeling or machine learning techniques could potentially reveal additional insights from the same set of data.