Time Series Forecasting Without Future Data: Empowering Decisions In The Face Of Uncertainty
Time Series Forecasting without Future Data (TSF No F) involves predicting future values of a time series when future data is unavailable. Despite the challenges, TSF No F leverages partial observability and employs diverse methods like extrapolation, machine learning, and ensemble techniques to handle missing future data. It provides point estimates, probability distributions, and handles confidence and uncertainty. TSF No F finds applications in demand forecasting, anomaly detection, financial modeling, and more, enabling organizations to make informed decisions despite the absence of future data.