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STL (Seasonal-Trend decomposition using LOESS) is a robust method for decomposing time series data, particularly when the seasonality is not fixed or follows irregular cycles, such as economic cycles. Unlike traditional methods like classical decomposition, which assume a fixed seasonal period, STL uses locally weighted regression (LOESS) to estimate the seasonal and trend components, making it flexible and capable of handling non-constant seasonal patterns. STL can handle both long-term trends and irregular, cyclical components, making it an ideal choice for data with variable seasonality or unpredictable cycles. Why Other Options Are Incorrect: • A: Classical decomposition assumes fixed seasonality and may not be effective for cyclical patterns that do not follow a regular period. • B: X-11 decomposition is a variation of classical decomposition and is also designed for regular seasonality, making it unsuitable for irregular cycles. • D: While Seasonal-Trend decomposition using LOESS (STL) is robust, it is the best option for irregular seasonality, making this method the most appropriate. • E: ARIMA decomposition is designed for models involving autoregressive, differencing, and moving averages but does not explicitly handle irregular seasonal or cyclical patterns.
1(1/2)+ 11(1/3) + 111(1/2) + 1111(1/3) + 11111(1/2) = ?
Find the simplified value of the following expression:
[{12 + (13 × 4 ÷ 2 ÷ 2) × 5 – 8} + 13 of 8]
3/4 of 2000 + √1024 = ? + 12.5% of 3200
32% of 450 + 60% of 150 = ? × 9
√4096 + 4/5 of 780 − ? = 296
9 × 40× 242 × 182= ?2
33 × 5 - ?% of 250 = 62 - 6
√ (573 – 819 + 775) = ? ÷ 3
If a nine-digit number 389x6378y is divisible by 72, then the value of √(6x + 7y) will be∶