2024-04-26 Turkmenistan New Manat News
2024-04-25
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
First and foremost, we will look at the overall trend of this time-series data. From the initial analysis, it can be concluded that the overall trend of the exchange rates over the surveyed time period indicates some form of stability with minor variations. The TMT exchange rate starts and ends at around 0.390. However, there exist tiny dips and surges between these points giving an appearance of a subtle hill-valley formation - indicating a fluctuating trend.
Recurring Patterns and Seasonality
It's difficult to directly infer seasonality or recurring patterns from this dataset as it only covers a single day's timeframe. For a deeper and more accurate identification of seasonality trends, data covering numerous cycles (typically, multiple years) would be more suitable. This helps in identifying recurrent yearly, quarterly, or monthly patterns, if any. Nonetheless, it's worth mentioning that some minor variations in the exchange rates can be observed at regular intervals indicating potential intraday patterns, which might be attributable to the trading behavior.
Outliers and Unexpected Rate Changes
No significant outliers or unexpected rate changes can be detected from the provided data. The exchange rate floats around 0.390 for the most part with frequent but minor fluctuations. The smallest observed rate is 0.38928 at the times 13:10:03 and 13:15:03, and the highest is 0.39107 around the 08:15:02 mark. These do not seem to be significant deviations to classify them as outliers, especially given the foreign exchange market is a highly volatile one.
To conclude, while this simple analysis provides some insights, for more advanced interpretations including volatility, trend strength, and predictive modeling, more sophisticated methods like statistical modeling and machine learning might be necessary.