Identify the models that are appropriate for univariate data: (A) Logit Regression (B) Exponential Smoothing (C) Moving Average (D) Auto-regressive
Choose the correct answer from the options given below:

Correct option is B

Explanation:
A univariate time series model deals with a single variable observed over time. The goal is to model its pattern and make forecasts.
Let’s evaluate each option:
· (A) Logit Regression
· Used for classification (typically binary outcomes like yes/no).
· Requires an independent variable, not typically used for time series univariate data.
· (B) Exponential Smoothing
· A forecasting method for univariate time series using weighted averages of past values.
· (C) Moving Average
· A smoothing technique that helps analyze trends in univariate time series data by averaging over time windows.
· (D) Auto-regressive (AR) model
· A standard univariate time series model, where the current value depends on its past values.
Information Booster:
· Exponential smoothing is best for data with a clear trend or seasonal component.
· Moving average smooths out short-term fluctuations and highlights long-term trends.
· AR models are part of ARIMA models used for forecasting time series.
Additional Information:
· Logit regression is generally used when the dependent variable is categorical, not continuous or time-dependent.
· Hence, only B, C, and D are suitable for modeling univariate data.