Malaysian Ringgit Forecast

Not for Invesment, Informational Purposes Only

Summary of Yesterday

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Statistical Measures

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    Analysis of MYR Exchange Rate

    The MYR exchange rate dataset given spans across a significant period providing rich time series data. The following insights were derived from the provided dataset:

    Understanding Overall Trend of Exchange Rates

    Upon the analysis of this dataset, it can be observed that the MYR exchange rate depicted a gradual increase during the evaluated period. The values initially started around 0.28506 and ended close to 0.2864. This suggests that the exchange rate generally increased over the given timeline.

    Identification of Seasonality or Recurrent Patterns

    Analysis for seasonality or recurring patterns in fine-grained time-series data over smaller periods requires additional computational tools and complex algorithms. However, from a high-level overview, no substantial recurring patterns or seasonality can be confirmed with this dataset alone.

    Outliers in the Dataset

    The examination of this dataset did not reveal any significant outliers. While there are fluctuations within the dataset, they appear to be part of the general trend and do not significantly deviate from the expected value. They are likely the result of normal market functions and do not indicate any abnormal changes in the MYR exchange rate over the timeline provided.

    You mentioned not to consider any external factors like market opening/closing hours, weekends/holidays, or the release of key financial news and reports. However, it's worth noting that these factors can have a significant impact on exchange rates and might explain any patterns or fluctuations in the data more precisely.

Summary of Yesterday

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Statistical Measures

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    Overall Trend of Exchange Rates

    The overall trend of the exchange rates indicated by the dataset fluctuates with no clear inclination towards a constant increase, decrease, or stability. The rates ranged from ~0.28440 to ~0.28527 throughout the period covered by the dataset. At several instances, there was a consistent increase/decrease in the rate, but it does not encapsulate the overall behavior of the dataset. Therefore, it's essential to perceive this as an unstable market condition where the exchange rates oscillate frequently.

    Seasonality or Recurring Patterns

    Based on the dataset provided, no clear seasonality or recurring patterns were observed in the exchange rates. The changes seem rather spontaneous and do not appear to follow a certain cyclical or seasonal pattern. However, considering a larger dataset spanning across different months or years might reveal potential seasonal trends that are not apparent in this current dataset.

    Outliers Detection

    Regarding the outliers or instances where the exchange rate deviates significantly from what is expected, there weren't such instances observed in this dataset. The exchange rate changes were mostly within a small interval around the mean exchange rate, indicating minimal volatility. There weren't any values significantly higher or lower than this range, suggesting that the fluctuations were mostly normal within this specific timeframe.

    In conclusion, the analysis reveals an unstable exchange rate with frequent fluctuations, lack of observable seasonality, and a minimal number of outliers. The exchange rates' movements thus might be highly influenced by numerous factors multifacetedly rather than recurrent elements or trends.

Summary of Yesterday

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Statistical Measures

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Summary of Last Month

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Statistical Measures

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    Overall Trend Analysis

    In this time series data, it appears that the MYR exchange rate stays relatively stable with minor fluctuations throughout this period. The rate starts at 0.28282 at the start of the period and ends at 0.28254 which is quite a negligible decrease over time. There's no significant upward or downward trend can be observed. What can be observed though is that the MYR exchange rate slightly fluctuates between points of 0.2825 - 0.2832.

    Seasonality and Recurring Patterns

    Seasonality or recurrence is a predictable change that repeats over time. In the given time-series data, it's difficult to determine a clear seasonal cycle or recurring pattern with only the timestamp provided. However, typically in financial data, there might exist daily or intraday cycles corresponding to market opening/closing hours.

    Identification of Outliers

    Outliers in data can distort predictions and affect the accuracy, if you don’t detect and handle them appropriately especially in linear regression models. Judging from the fluctuation range, it doesn't seem like there are any outliers or any extreme deviation from the general trend as the fluctuation seems to be in a tight range between 0.28250 - 0.2832, meaning all values lie close to each other. Hence, it can be concluded that there doesn't seem to be any significant outliers in this dataset.

Summary of Last Week

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Statistical Measures

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    Overall Trend of the Exchange Rates

    Upon initial visual assessment of this data, the MYR exchange rate seems to be relatively stable over the given period, fluctuating around 0.281 to 0.285. There is no apparent consistent increase or decrease trend that might suggest appreciation or depreciation of the currency.

    Seasonality or Recurring Patterns in the Exchange Rates

    It is challenging to infer any seasonality or recurring patterns from the given data without proper visualization tools. However, no obvious daily or weekly patterns occur frequently enough to suggest a recurring trend. The variances seem to be quite random and do not appear to follow any predictable patterns.

    Identification of Outliers

    As per the provided data set, most fluctuation in the MYR exchange rate is subtle, and significant deviations from the trendline are infrequent. Any substantial upward or downward spikes can be considered an outlier. Given the data, there do not appear to be any extreme outliers. However, without a statistical analysis, determining the existence of minor outliers is challenging.

    Note

    This analysis is purely based on the given numerical dataset. No external factors such as market opening/closing hours, weekends/holidays, or the release of key financial news and reports have been considered. It is essential to understand that real-world exchange rates are susceptible to many such factors and more.

Summary of Yesterday

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Statistical Measures

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    1. Overview and Trend Analysis

    Going through the dataset from 19th February 2024 to 23rd February 2024, the overall rates appear to fluctuate within a narrow range (0.28 – 0.282). From an overall perspective, the exchange rates seem to slightly increase in this period. In the beginning, the rate is 0.28166, peaking at 0.28291 before settling around 0.28259. The general trend of the rate is moderately increasing in this specific timeframe; however, the fluctuations are quite small and relatively stable.

    2. Seasonality and Recurring Patterns

    Considering the time series nature of the data, the data doesn't show strong signs of seasonality or recurring patterns over the period analyzed. The data seem more or less random, and there may not be a particular recurring pattern on a daily, hourly, or any timeframe basis. This might suggest that short-term exchange rate movements are influenced by a wide variety of frequent and unpredictable factors, which can not be discerned from the provided dataset.

    3. Outliers and Volatility

    The dataset doesn't display any apparent evidence of outliers, with most values falling within a tight range of 0.28 to 0.282. There is a certain level of volatility or fluctuation over time, which is very typical for financial time series data like exchange rates. However, I wouldn't consider this as outliers necessarily - it is more of a natural characteristic of financial market data. The highest exchange rate observed was 0.28291, while the lowest was 0.28097, displaying slight variability in the rates during this period, but without significant deviations.

    Conclusion

    To summarize, this particular set of exchange rate data shows slight increments over the period from 19th February 2024 to 23rd February 2024. The data does not seem to indicate discernable patterns or seasonality. Also, there were no significant outliers or anomalous values seen in the dataset. It is important to note that the interpretation of these data has been done outside of the context of any known external events or factors that may potentially influence exchange rates.

Summary of Yesterday

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Statistical Measures

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  • Trend

    Overall Trend

    Upon reviewing the provided dataset, it is evident that the exchange rate has increased slightly across the observation period. The rates have moved from 0.28194 to 0.28262. While this growth might appear modest, it is necessary to bear in mind that even slight changes in the exchange rates can have extensive impacts on transactions of significant sizes.

    Seasonality and Reccuring Patterns

    I observed no clear seasonality or recurring patterns in this dataset. The exchange rates fluctuate relatively consistently across the timescale, with no apparent cyclical or regular repeating trends that could link to hourly, daily, or other periodic intervals.

    Outliers

    In regards to outliers, the dataset shows several instances where the exchange rate made small significant shifts compared to the periods immediately before and after. However, these cases are not extreme enough to be classified as true outliers. They could be a result of normal market volatility and not necessarily indicative of a significant unexpected change.

    Please keep in mind, this analysis did not consider specific external events such as market opening/closing hours, weekends/holidays, or the release of key financial news and reports. Including such factors might yield deeper insights into trends or outliers within the exchange rate changes.

    Note: Always consider seeking advice from a financial professional before making significant decisions based on exchange rate trends.