Time Matters: Error Metrics for Temporal Data and Time Series Forecasting

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Time series forecasting presents unique challenges in error evaluation due to the temporal dependencies inherent in the data. Traditional evaluation metrics may not fully capture the performance of forecasting models in this context. Here are some challenges associated with error evaluation in time series forecasting and metrics tailored to address these challenges:

I. Challenges in Time Series Forecasting Evaluation:

1. Temporal Dependency:

• Time series data often exhibit temporal dependencies, where the value at a given time point is related to past observations. Traditional metrics may not account for the importance of forecasting accurate values at specific time points.

2. Varying Seasonality:

• Time series data often exhibit seasonality, i.e., periodic patterns or cycles. Evaluation metrics need to be sensitive to capturing the accuracy of forecasting these periodic patterns.

3. Forecasting Horizons:

• Forecasting models are often evaluated over multiple time steps into the future (forecast horizon). Metrics need to consider the accuracy of predictions at various forecast horizons.