Incorporating uncertainty and randomness through probabilistic components in time series forecasting models can have significant implications for long-term forecasting accuracy. By introducing stochasticity into the model's predictions, probabilistic components allow for a more realistic representation of real-world variability and unpredictability present in many time series datasets. This incorporation enables the model to provide not only point estimates but also probability distributions over future outcomes, offering valuable insights into prediction confidence levels.
Furthermore, by accounting for uncertainty through probabilistic modeling, long-term forecasts become more robust against unexpected fluctuations or outliers in the data. The ability to capture inherent uncertainties inherent uncertainties allows for more reliable risk assessment and decision-making processes based on forecasted outcomes.