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Moving Average Time Series Forecasting with Excel
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@Matt Macarty
#Excel #Forecasting #TimeSeries #DataAnalysis #BusinessIntelligence
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Forecast Moving Average Time Series Analysis
Part I of Introductory Time Series Forecasting Series
Introduction to Time Series Forecasting with Moving Averages
✅ Part II & III can be found at the links below:
Forecasting with Exponential Smoothing and Weighted moving average:
Testing the quality of the forecast with Theil's U:
Introduction to time series forecasting using examples of moving average forecasting. We attempt to forecast the price of Gold using the GLD ETF as a proxy for the price of gold. Includes a discussion of commonly used error measures, mean absolute deviation (MAD), mean squared error (MSE, RMSE) and mean absolute percent error (MAPE). Error measures are used to determine how good your forecast is, in other words, they measure how far off your forecast is on average.
Learn how to utilize the power of Excel to forecast future trends using Moving Average techniques. This video will guide you through the step-by-step process of implementing Simple Moving Average (SMA) methods.
You'll discover:
Understanding Time Series Data: Grasp the fundamentals of time series data and its components.
Simple Moving Average (SMA): Learn how to calculate SMAs and interpret their results.
Weighted Moving Average (WMA): Explore the concept of assigning weights to recent data points for more accurate forecasts.
Excel Implementation: Step-by-step guidance on using Excel functions and formulas to calculate moving averages and generate forecasts.
Model Evaluation: Assess forecast accuracy using key metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE).
Real-world Applications: See how these techniques can be applied to various business scenarios, from sales forecasting to inventory management.
Whether you're a beginner or an experienced analyst, this video will equip you with the skills to make informed decisions based on data-driven insights.
#Excel #Forecasting #TimeSeries #DataAnalysis #BusinessIntelligence
✅ Please SUBSCRIBE:
Forecast Moving Average Time Series Analysis
Part I of Introductory Time Series Forecasting Series
Introduction to Time Series Forecasting with Moving Averages
✅ Part II & III can be found at the links below:
Forecasting with Exponential Smoothing and Weighted moving average:
Testing the quality of the forecast with Theil's U:
Introduction to time series forecasting using examples of moving average forecasting. We attempt to forecast the price of Gold using the GLD ETF as a proxy for the price of gold. Includes a discussion of commonly used error measures, mean absolute deviation (MAD), mean squared error (MSE, RMSE) and mean absolute percent error (MAPE). Error measures are used to determine how good your forecast is, in other words, they measure how far off your forecast is on average.
Learn how to utilize the power of Excel to forecast future trends using Moving Average techniques. This video will guide you through the step-by-step process of implementing Simple Moving Average (SMA) methods.
You'll discover:
Understanding Time Series Data: Grasp the fundamentals of time series data and its components.
Simple Moving Average (SMA): Learn how to calculate SMAs and interpret their results.
Weighted Moving Average (WMA): Explore the concept of assigning weights to recent data points for more accurate forecasts.
Excel Implementation: Step-by-step guidance on using Excel functions and formulas to calculate moving averages and generate forecasts.
Model Evaluation: Assess forecast accuracy using key metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE).
Real-world Applications: See how these techniques can be applied to various business scenarios, from sales forecasting to inventory management.
Whether you're a beginner or an experienced analyst, this video will equip you with the skills to make informed decisions based on data-driven insights.
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