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Processing method of time series data:

Smoothing method: Smooth data using a sliding window or filter to remove noise or outliers.

Interpolation: Interpolating algorithm is used to fill in missing values.

Difference method: Difference sequential data to obtain a first or higher order difference to remove trend or seasonal components.

decomposition method: Time series decomposition method is used to decompose time series data into trend, seasonality, periodicity and residual components for further analysis or prediction.

Feature extraction method: extract features of time series data, such as lag, moving average, volatility, etc., to generate new feature vectors.

Machine learning method: Machine learning algorithm is used for classification, regression, clustering and other analysis of time series data to find potential rules and relationships.

can do feature derivation:

Statistical characteristics: mean, variance, maximum, minimum, percentile, mean difference, etc.

Sliding window features: sliding average, sliding variance, sliding maximum, sliding minimum, etc.

Periodic characteristics: time stamp, day of the week, month, season, etc.

Aggregate characteristics: mean, variance, maximum, minimum, etc., within the same category or group.

Offset feature: The value of the point in time before or after the point in time.

Time sequence characteristics: lag, difference, moving average, exponential smoothing, etc.

Diverse transformations: logarithm, power, reciprocal, quadratic, etc.

Some Python packages that handle timing data:

pandas: Pandas are used to handle time series data, supporting resampling, sliding window, moving average, etc.

numpy: Used to handle numerical calculations, providing a variety of numerical operations and functions.

statsmodels: For time series modeling and analysis, various models and methods are provided, such as ARIMA, VAR, cointegration, etc.

scikit-learn: For machine learning, Scikit-Learn provides a variety of classification, regression, clustering, dimensionality reduction algorithms for time series prediction and analysis.

prophet: A time series forecasting tool developed by Facebook for seasonal and trend decomposition and prediction using flexible non-parametric models.