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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.