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Exploring Time Series Analysis

Authored by Praveen Loharkar

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University

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Exploring Time Series Analysis
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20 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is seasonal decomposition in time series analysis?

Seasonal decomposition is the process of separating a time series into trend, seasonal, and residual components.

Seasonal decomposition involves averaging the data over a fixed period.

Seasonal decomposition is a method for predicting future values in a time series.

Seasonal decomposition is the process of combining multiple time series into one.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the components of a time series in seasonal decomposition.

trend, seasonality, and linearity

trend, seasonality, volatility, and noise

trend, seasonality, and randomness

The components of a time series in seasonal decomposition are trend, seasonality, cyclicality, and irregularity.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does ARIMA stand for and what are its components?

ARIMA stands for AutoRegressive Integrated Moving Average.

AutoRegressive Integrated Moving Average Model

AutoRegressive Moving Average Integrated

Average Integrated Moving Auto Regression

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How do you determine the order of differencing in an ARIMA model?

Differencing is not necessary if the data is already stationary.

The order of differencing is based on the length of the time series.

The order of differencing (d) is determined by the number of times differencing is applied to achieve stationarity.

The order of differencing is determined by the model's seasonal component.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of exponential smoothing in forecasting?

The purpose of exponential smoothing in forecasting is to provide accurate predictions by giving more weight to recent data.

To focus solely on long-term trends without considering recent changes.

To create a static model that does not change over time.

To eliminate all past data from the forecast.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Describe the difference between simple and double exponential smoothing.

Simple exponential smoothing uses one smoothing constant for level, while double exponential smoothing uses two constants for level and trend.

Simple exponential smoothing is more complex than double exponential smoothing.

Double exponential smoothing requires only one constant for both level and trend.

Simple exponential smoothing is used for seasonal data, while double exponential smoothing is for non-seasonal data.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the autocorrelation function (ACF) measure in a time series?

The ACF measures the overall volatility of a time series.

The ACF measures the difference between consecutive values in a time series.

The ACF measures the correlation of a time series with its own past values.

The ACF measures the trend of a time series over time.

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