Data Mining Methods(6)

 Bilal Hussain Malik

                                                        Data Mining Methods


                                                  Time Series Analysis

Time series analysis examines serial points of data that are collected over time in order to find patterns, trends, and seasonality. Projections of future values based on historical data require it. Exponential smoothing is used for trend-based projections, and ARIMA (AutoRegressive Integrated Moving Average) is for stationary data. Its applications involve stock market forecasting, weather forecasting, and inventory management. Missing data handling, noise removal, and anomaly detection are some of the main challenges. The method is particularly valuable in applications in which temporal trends play a significant impact on results, such as economic indicators or equipment sensor readings. Advanced methods now reach into machine learning to improve accuracy for challenging, non-linear time series data.

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