How can I handle missing values in time series data for accurate forecasting?

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How can I handle missing values in time series data for accurate forecasting?

Asked on Dec 30, 2025

Answer

Handling missing values in time series data is crucial for accurate forecasting, as gaps can lead to biased models and inaccurate predictions. Techniques such as interpolation, forward/backward filling, or using model-based imputation can be employed depending on the data's characteristics and the forecasting model used.

Example Concept: Interpolation is a common method for handling missing values in time series data, where missing points are estimated based on surrounding data points. Linear interpolation assumes a straight line between known values, while spline interpolation uses piecewise polynomials for a smoother fit. Forward and backward filling are simpler methods where missing values are replaced by the last or next observed value, respectively. Model-based imputation involves using statistical or machine learning models to predict missing values based on observed data patterns.

Additional Comment:

Choose the imputation method based on the time series' nature and the forecasting model's sensitivity to missing data.
Evaluate the impact of imputation on model performance using cross-validation or a holdout validation set.
Consider domain knowledge and seasonality when selecting an imputation method, as these factors can influence data patterns.

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