Time Series Analysis
Time Series Analysis is a statistical technique used to analyze and extract meaningful patterns and insights from time series data. It involves identifying patterns and trends in the data, as well as forecasting future values based on historical trends. Time series analysis is widely used in various fields such as finance, economics, weather forecasting, and social sciences. The analysis involves decomposing the time series into its components, such as trend, seasonality, and noise, and then modeling each component separately. The models used in time series analysis include ARIMA, exponential smoothing, and state space models. Time series analysis is also used for anomaly detection, identifying outliers and unusual patterns in the data.
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