Example Concept: Common pitfalls in time series forecasting include overfitting, ignoring seasonality, and failing to account for non-stationarity. Overfitting occurs when a model is too complex and captures noise instead of the underlying pattern, which can be mitigated by using simpler models or regularization techniques. Ignoring seasonality can lead to inaccurate forecasts, so it's crucial to identify and incorporate seasonal patterns into the model. Non-stationarity, where statistical properties change over time, can be addressed by differencing the data or using models that handle trends and seasonality, such as ARIMA or SARIMA.