Solving Time Series Forecasting Problems: Principles and Techniques

This article delves into time series analysis, discussing its significance in decision-making processes. It elucidates various techniques such as cross-validation, decomposition, and transformation of time series, as well as feature engineering. It provides a deep understanding of different modeling approaches, including but not limited to, Exponential Smoothing, ARIMA, Prophet, Gradient Boosting, Recurrent Neural Networks (RNNs), N-BEATS, and Temporal Fusion Transformers (TFT). Despite the wide range of techniques covered, the article emphasizes the need for experimentation to choose the method that …

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