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Questions and Answers
What does a stationary time series indicate about its statistical properties?
What does a stationary time series indicate about its statistical properties?
In which forecasting method is the average of the most recent k data values used to predict the next period?
In which forecasting method is the average of the most recent k data values used to predict the next period?
What characterizes a seasonal pattern in a time series?
What characterizes a seasonal pattern in a time series?
What does the mean absolute percentage error (MAPE) measure in a time series?
What does the mean absolute percentage error (MAPE) measure in a time series?
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Which of the following best describes a trend pattern in a time series?
Which of the following best describes a trend pattern in a time series?
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What is required when using weighted moving averages for forecasting?
What is required when using weighted moving averages for forecasting?
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What does forecast error represent in time series analysis?
What does forecast error represent in time series analysis?
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What does the smoothing constant in exponential smoothing influence?
What does the smoothing constant in exponential smoothing influence?
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Which characteristic identifies a recurrent pattern in data over specific periods in a time series?
Which characteristic identifies a recurrent pattern in data over specific periods in a time series?
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What does the mean squared error (MSE) assess in a time series forecasting model?
What does the mean squared error (MSE) assess in a time series forecasting model?
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What is the main difference between moving averages and weighted moving averages?
What is the main difference between moving averages and weighted moving averages?
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In a time series analysis, how can one determine the presence of a trend?
In a time series analysis, how can one determine the presence of a trend?
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What does the term 'forecast error' refer to?
What does the term 'forecast error' refer to?
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Which forecasting method involves selecting one weight for the latest observation?
Which forecasting method involves selecting one weight for the latest observation?
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What is a defining feature of a stationary time series?
What is a defining feature of a stationary time series?
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What does the smoothing constant influence in an exponential smoothing model?
What does the smoothing constant influence in an exponential smoothing model?
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Study Notes
Time Series
- A sequence of observations on a variable measured over time or successive periods.
Time Series Plot
- Shows the relationship between time and the time series variable.
- Time on the horizontal axis.
- Time series values on the vertical axis.
Stationary Time Series
- Statistical properties do not change over time.
- Constant mean.
- Constant variability.
Trend Pattern
- Gradual upward or downward movement over a longer period in a time series plot.
Seasonal Pattern
- Repeating pattern over successive periods, often yearly.
Cyclical Pattern
- Alternating sequence of points above and below the trend line, lasting more than one year.
Forecast Error
- Difference between actual and forecast values.
Forecasting Metrics
- Mean Absolute Error (MAE): Average of absolute forecast errors.
- Mean Squared Error (MSE): Average of squared forecast errors.
- Mean Absolute Percentage Error (MAPE): Average of absolute percentage forecast errors.
Moving Averages
- Forecasting method using the average of the most recent k data values.
Weighted Moving Averages
- Forecasting method assigning different weights to recent data points.
- Sum of weights equals 1.
Exponential Smoothing
- Forecasting method using a weighted average of past values.
- Special case of weighted moving averages using only one weight (most recent).
Smoothing Constant
- Parameter in exponential smoothing.
- Weight assigned to the most recent time series value.
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Description
Dive into the essentials of time series analysis with this quiz. Covering key topics such as stationary series, trend patterns, and forecasting metrics, it will help you grasp the foundational concepts of analyzing data over time. Perfect for students and professionals looking to enhance their understanding of time series data.
