Big Data and AI in Operations Management: Time Series Forecasting

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What does the author need to do if the residuals are not white noise?

Decrease the number of lags in the model

Change the parameters until the residuals become white noise (correct)

Ignore the residuals and continue with the current model

Increase the number of lags in the model

What does the author do after seeing that the residuals are correlated?

They go back to the PACF to adjust the parameter p (correct)

They increase the number of lags in the model

They decrease the number of lags in the model

They go back to the ACF to adjust the parameter p

What is the author's conclusion about the residuals after trying p=8?

The residuals are normally distributed

The residuals are not white noise

The residuals are white noise (correct)

The residuals are still correlated

What does the author state about the PACF compared to the ACF for the seasonal data?

The PACF is simpler than the ACF (A) Signup and view all the answers

What is the final model the author proposes for the seasonal data?

(3,2,0)*(1,0,0)s=4 (D) Signup and view all the answers

What is the goal of improving the prediction of electricity consumption?

To reduce the penalty paid for larger deviations (A) Signup and view all the answers

What is the main purpose of the Box Jenkins methodology?

To identify ARIMA models in time series data (B) Signup and view all the answers

How can you identify a pure autoregressive model?

By considering only one PACF lag out of bounds (C) Signup and view all the answers

What does the constant term in the model represent?

A statistically significant value different from zero (A) Signup and view all the answers

What is the most appropriate way to create 95% confidence intervals for 1 step ahead point predictions?

Multiplying the standard error by 1.96 (B) Signup and view all the answers

What does AR(p) stand for in the context of time series modeling?

Auto-Regressive model with p lags (A) Signup and view all the answers

Why is it important to start with a simple model in time series analysis?

To avoid overfitting and understand the data better (D) Signup and view all the answers

What is the main focus of working with time series data in operations management?

Predicting the future values of a random variable (A) Signup and view all the answers

In time series data, why is it important to give more weight to recent observations?

Patterns may fluctuate and evolve with time (B) Signup and view all the answers

What does the stationarity condition in time series data assume?

Mean and variance are constant (C) Signup and view all the answers

What is the purpose of the lag operator in transforming time series data?

To obtain the value at time t (D) Signup and view all the answers

What does strict white noise imply?

Independent data with no relationships (B) Signup and view all the answers

Why is it important to visualize time series data before applying models?

To identify trends that may not be visible in numerical data (C) Signup and view all the answers

What does a kurtosis value close to zero indicate about a dataset?

It is close to a normal distribution (A) Signup and view all the answers

What does a Shapiro test for in data analysis?

Normality of data distribution (A) Signup and view all the answers

What does a Rank test examine in data?

Independence of data points (A) Signup and view all the answers

What does having skewness and kurtosis close to zero suggest about a dataset?

The dataset is symmetric and close to normal distribution (D) Signup and view all the answers

What is the relationship between the residuals e(t) and the mean?

The residuals e(t) can be either stationary or non-stationary in the mean (D) Signup and view all the answers

If the residuals e(t) are stationary in the mean, what can be said about them?

They can be either White Noise (WN) or not White Noise (WN) (C) Signup and view all the answers

What is the meaning of a 'consistent estimator'?

The estimated parameter value gets closer to the true value as the sample size increases (C) Signup and view all the answers

What is the implication if a model is not consistent?

The model parameters are not good and cannot be trusted (B) Signup and view all the answers

What should be checked for the residuals of an OLS model?

The residuals should be independent and identically distributed (IID) (A) Signup and view all the answers

What is the purpose of accounting for temperature/weather in the regression model?

To improve the accuracy of electricity price forecasts (A) Signup and view all the answers

What is the purpose of the regression model $P(t) = a + B d(t) + e(t)$?

To model the relationship between electricity price and demand (D) Signup and view all the answers

What is the purpose of the model $e(t) = 0 + 1 e(t-1) + q(t)$?

To model the relationship between the residuals and the past residuals (D) Signup and view all the answers

What is the purpose of the combined model $P(t) = a + B d(t) + 1 (P(t-1) - a - B d(t-1)] + q(t)$?

To model the relationship between electricity price and demand, including the effect of past prices (B) Signup and view all the answers

What is the purpose of the first step mentioned in the text?

To estimate the best regression model for electricity price as a function of demand, wind production, and gas price (B) Signup and view all the answers

What is the key advantage of using the regression model with time series errors over the SARIMA model?

The regression model can incorporate the effects of temperature/weather, while the SARIMA model cannot (A) Signup and view all the answers