Evaluating dataset quality for time series forecasting involves checking three key areas: data completeness and consistency, temporal structure, and relevance of features. Start by ensuring the dataset has no gaps or irregularities. Time series forecasting relies on sequential observations, so missing timestamps or inconsistent sampling intervals (e.g., hourly data mixed with daily data) can break model assumptions. For example, if you’re working with hourly temperature data but some days have only 10 entries due to sensor failures, interpolation or imputation might be necessary. Similarly, check for outliers or anomalies—like sudden spikes in sales data due to one-time promotions—that could mislead the model. Tools like pandas in Python can help visualize gaps and calculate missing value percentages.
Next, analyze the temporal structure of the data. A good time series dataset should exhibit patterns the model can learn, such as trends, seasonality, or cycles. For instance, retail sales data often has weekly seasonality (higher sales on weekends) and yearly trends (holiday spikes). Use statistical tests like the Augmented Dickey-Fuller test to check for stationarity (consistent mean and variance over time). Non-stationary data might require differencing or transformations. Also, ensure the dataset covers a sufficient time span. Predicting monthly electricity demand with only three months of data is problematic because models need enough cycles to capture recurring patterns. If the data is too short, consider synthetic data generation or transfer learning.
Finally, verify the relevance and quality of features. In multivariate forecasting, features must have a logical relationship to the target variable. For example, including humidity data when predicting bike rentals can improve accuracy, but adding unrelated metrics (e.g., stock prices) adds noise. Use domain knowledge and correlation analysis to filter features. Also, check for data leakage—features that include future information, like including tomorrow’s temperature in today’s weather dataset. Normalize or scale features if they have vastly different ranges (e.g., temperature in °C vs. sales revenue in thousands). Tools like autocorrelation plots or feature importance scores from models like XGBoost can help identify useful predictors. A well-structured dataset with clean, relevant features is foundational for reliable forecasts.