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The time series forecasting method that gives equal weightage to each of the M mostrecent observation is
- a)Moving average method
- b)Exponential smoothing with linear trend.
- c)Triple Exponential smoothing
- d)Kalman Filter
Correct answer is option 'A'. Can you explain this answer?
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The time series forecasting method that gives equal weightage to each ...
‘Simple moving average method’ is generally also called ‘Moving average method’ which gives equal weightage to all data points for period ‘M’ on which it is defined.
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The time series forecasting method that gives equal weightage to each ...
Answer:
The time series forecasting method that gives equal weightage to each of the M most recent observations is the moving average method.
Explanation:
The moving average method is a commonly used time series forecasting technique that calculates the average of a specified number of consecutive observations to predict future values. In this method, each observation is given equal weightage.
Steps in the Moving Average Method:
1. Identify the number of observations to be included: In the moving average method, a fixed number of the most recent observations are considered for forecasting. Let's call this number M.
2. Calculate the average of the M most recent observations: Add up the values of the M most recent observations and divide the sum by M to calculate the moving average.
3. Repeat the process for each new observation: As new observations become available, remove the oldest observation from the calculation and add the new observation to the calculation. Recalculate the moving average at each step.
4. Forecast future values: Using the calculated moving average, forecast future values by assuming that the average will continue to hold true for the upcoming observations.
The moving average method is useful when the time series data is non-linear and exhibits random fluctuations. It is a simple and easy-to-understand method that provides a smoothed representation of the data.
Advantages of the Moving Average Method:
- Easy to implement and understand.
- Suitable for time series data with random fluctuations.
- Smooths out short-term fluctuations, making it easier to identify long-term trends.
Limitations of the Moving Average Method:
- Does not capture complex patterns or trends in the data.
- May not perform well with time series data that has a changing trend or seasonality.
- Relies heavily on past observations and may not be suitable for future forecasting.
In conclusion, the moving average method gives equal weightage to each of the M most recent observations and is useful for forecasting time series data with random fluctuations.
The time series forecasting method that gives equal weightage to each of the M most recent observations is the moving average method.
Explanation:
The moving average method is a commonly used time series forecasting technique that calculates the average of a specified number of consecutive observations to predict future values. In this method, each observation is given equal weightage.
Steps in the Moving Average Method:
1. Identify the number of observations to be included: In the moving average method, a fixed number of the most recent observations are considered for forecasting. Let's call this number M.
2. Calculate the average of the M most recent observations: Add up the values of the M most recent observations and divide the sum by M to calculate the moving average.
3. Repeat the process for each new observation: As new observations become available, remove the oldest observation from the calculation and add the new observation to the calculation. Recalculate the moving average at each step.
4. Forecast future values: Using the calculated moving average, forecast future values by assuming that the average will continue to hold true for the upcoming observations.
The moving average method is useful when the time series data is non-linear and exhibits random fluctuations. It is a simple and easy-to-understand method that provides a smoothed representation of the data.
Advantages of the Moving Average Method:
- Easy to implement and understand.
- Suitable for time series data with random fluctuations.
- Smooths out short-term fluctuations, making it easier to identify long-term trends.
Limitations of the Moving Average Method:
- Does not capture complex patterns or trends in the data.
- May not perform well with time series data that has a changing trend or seasonality.
- Relies heavily on past observations and may not be suitable for future forecasting.
In conclusion, the moving average method gives equal weightage to each of the M most recent observations and is useful for forecasting time series data with random fluctuations.
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The time series forecasting method that gives equal weightage to each of the M mostrecent observation isa)Moving average methodb)Exponential smoothing with linear trend.c)Triple Exponential smoothingd)Kalman FilterCorrect answer is option 'A'. Can you explain this answer?
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