Big Data and AI in Operations Management: Time Series Forecasting
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Questions and Answers

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?

    <p>The PACF is simpler than the ACF</p> Signup and view all the answers

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

    <p>(3,2,0)*(1,0,0)s=4</p> Signup and view all the answers

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

    <p>To reduce the penalty paid for larger deviations</p> Signup and view all the answers

    What is the main purpose of the Box Jenkins methodology?

    <p>To identify ARIMA models in time series data</p> Signup and view all the answers

    How can you identify a pure autoregressive model?

    <p>By considering only one PACF lag out of bounds</p> Signup and view all the answers

    What does the constant term in the model represent?

    <p>A statistically significant value different from zero</p> Signup and view all the answers

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

    <p>Multiplying the standard error by 1.96</p> Signup and view all the answers

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

    <p>Auto-Regressive model with p lags</p> Signup and view all the answers

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

    <p>To avoid overfitting and understand the data better</p> Signup and view all the answers

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

    <p>Predicting the future values of a random variable</p> Signup and view all the answers

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

    <p>Patterns may fluctuate and evolve with time</p> Signup and view all the answers

    What does the stationarity condition in time series data assume?

    <p>Mean and variance are constant</p> Signup and view all the answers

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

    <p>To obtain the value at time t</p> Signup and view all the answers

    What does strict white noise imply?

    <p>Independent data with no relationships</p> Signup and view all the answers

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

    <p>To identify trends that may not be visible in numerical data</p> Signup and view all the answers

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

    <p>It is close to a normal distribution</p> Signup and view all the answers

    What does a Shapiro test for in data analysis?

    <p>Normality of data distribution</p> Signup and view all the answers

    What does a Rank test examine in data?

    <p>Independence of data points</p> Signup and view all the answers

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

    <p>The dataset is symmetric and close to normal distribution</p> Signup and view all the answers

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

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

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

    <p>They can be either White Noise (WN) or not White Noise (WN)</p> Signup and view all the answers

    What is the meaning of a 'consistent estimator'?

    <p>The estimated parameter value gets closer to the true value as the sample size increases</p> Signup and view all the answers

    What is the implication if a model is not consistent?

    <p>The model parameters are not good and cannot be trusted</p> Signup and view all the answers

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

    <p>The residuals should be independent and identically distributed (IID)</p> Signup and view all the answers

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

    <p>To improve the accuracy of electricity price forecasts</p> Signup and view all the answers

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

    <p>To model the relationship between electricity price and demand</p> Signup and view all the answers

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

    <p>To model the relationship between the residuals and the past residuals</p> Signup and view all the answers

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

    <p>To model the relationship between electricity price and demand, including the effect of past prices</p> Signup and view all the answers

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

    <p>To estimate the best regression model for electricity price as a function of demand, wind production, and gas price</p> Signup and view all the answers

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

    <p>The regression model can incorporate the effects of temperature/weather, while the SARIMA model cannot</p> Signup and view all the answers

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