Solving Time Series Forecasting Problems: Principles and Techniques

Data Science Tech Brief By HackerNoon

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Explore time series analysis: from cross-validation, decomposition, transformation to advanced modeling with ARIMA, Neural Networks, and more.
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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 yields the best performance given the data characteristics and problem specifics.