Exploring Time Series Analysis

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.

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.

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

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.

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.

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.

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.

How can you use the ACF to identify the presence of seasonality?

• Significant spikes in the ACF plot indicate randomness.

• The presence of seasonality can be identified by observing significant spikes at regular intervals in the ACF plot.

• ACF is used to measure the mean of the data.

• Seasonality can be identified by a flat ACF plot.

What is stationarity in the context of time series data?

• Stationarity means that the data is collected at regular intervals only.

• Stationarity indicates that a time series is always increasing.

• Stationarity refers to the presence of seasonal patterns in the data.

• Stationarity means that the statistical properties of a time series do not change over time.

List two common tests for checking stationarity in time series data.

• Chi-Squared test

• Augmented Dickey-Fuller (ADF) test, Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test

• Shapiro-Wilk test

• Mann-Kendall test