Event Prediction in Time-series Data: Analyzing techniques for event prediction in time-series data, such as forecasting market trends or detecting anomalies
Keywords:
Time-series data, Event predictionAbstract
Event prediction in time-series data is crucial for various applications, including forecasting market trends, detecting anomalies, and predicting natural phenomena. This paper provides a comprehensive analysis of techniques for event prediction in time-series data. We review traditional methods such as autoregressive models, moving averages, and exponential smoothing, as well as modern approaches including machine learning and deep learning models. We also discuss the challenges and future directions in event prediction, emphasizing the importance of interpretability and scalability in real-world applications.
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