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The ARIMA model is widely used for time series forecasting because it combines two key components: autoregressive (AR), which uses past data points to model future values, and moving average (MA), which smooths out short-term fluctuations in the data. Additionally, integration (I) is used to make a non-stationary time series stationary by differencing the data. This allows ARIMA to be applied to a wide range of time series data, even if they exhibit complex patterns, provided the data can be made stationary. Option A is incorrect because ARIMA requires the data to be stationary (or at least made stationary through differencing). Option B is incorrect because ARIMA can handle data with both long-term trends and periodic fluctuations. Option D is incorrect because ARIMA is not the best model for time series with seasonal components—SARIMA (Seasonal ARIMA) is more appropriate for that. Option E is incorrect because ARIMA can handle irregular components as long as the data is stationary or can be made stationary.
In the following questions, there are three columns, and each column contains three phrases. Choose one phrase from each column to create a sentence tha...
In the following questions, two columns are given, Column 1 and Column 2. Each column contains 3 phrases. Match the phrases in Column 1 with the phrase...
Match the words with their meaning.
Words Meanings
a. Fathom 1. Team
b. Aeons 2. Understand ...
(A)In India, millions of people don’t ... Column (1) Two columns are given in each question and each column has three parts of a sentence. Choose the most suitable pair, which makes a grammatically correct... |