GITNUX MARKETDATA REPORT 2023
Must-Know Forecasting Accuracy Metrics
Highlights: The Most Important Forecasting Accuracy Metrics
- 1. Mean Absolute Error (MAE)
- 2. Mean Squared Error (MSE)
- 3. Root Mean Squared Error (RMSE)
- 5. Mean Absolute Scaled Error (MASE)
- 7. Mean Directional Accuracy (MDA)
- 8. Mean Absolute Deviation (MAD)
- 9. Median Absolute Deviation (MedAD)
- 10. Mean Error (ME) or Bias
- 11. Theil’s U-statistic
- 12. Diebold-Mariano test
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Forecasting Accuracy Metrics: Our Guide
Dive into our latest report and navigate the world of Forecasting Accuracy Metrics with confidence. This blog post outlines essential metrics that any data science professional should master to accurately predict future business trends and outcomes. Stay ahead with our updated insights on key forecasting accuracy metrics, helping you drive smarter business decisions.
Moving Average
Simple forecasting metric: Uses the average of past data points for predictions; simple but lacks trend and seasonality capture.
Exponential Smoothing
Weighted Moving Average: Assigns more weight to recent data for better trend capture, requires less historical data.
Seasonal Decomposition Of Time Series
Decomposition Metric: Separates time series into trend, seasonal, and residual parts, improving accuracy by considering seasonality.
Autoregressive Integrated Moving Average
ARIMA Model: Combines AR and MA models, along with differencing for stationarity, to forecast based on past-present relationships.
Seasonal ARIMA
SARIMA: Extends ARIMA with seasonal differencing and terms, ideal for seasonal time series.
Holt-Winters Method
Triple Exponential Smoothing: Adds trend and seasonality to exponential smoothing for forecasting time series with trends and seasonality.
Vector Autoregression
Multivariate AR Model: Extends AR to multiple time series, ideal for simultaneous forecasting of multiple series.
Bayesian Structural Time Series
Probabilistic Bayesian Model: Enhances accuracy by incorporating uncertainty and external data, suitable for limited historical data.
Croston’s Method
Croston’s Method: Designed for irregular, intermittent demand forecasting by separately predicting demand size and intervals.
Neural Networks
Neural Networks: Al and ML for complex, large-scale time series forecasting with learned patterns.
Prophet
Facebook’s Forecasting: Handles irregular trends, seasonality, and holidays using automatic model selection.
Time Series Decomposition
Time Series Decomposition: Breaks down data into components (trend, seasonal, etc.) for accurate forecasting.
Frequently Asked Questions
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How we write these articles
We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly. See our Editorial Guidelines.
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