TensorFlow Time Series: TensorFlow时间序列(TFTS)

TensorFlow Time Series (TFTS) is a collection of ready-to-use classic models (state space, autoregressive), and flexible infrastructure for building high-performance time series models whatever the architecture. It includes tools for chunking and batching a series, and for saving model state across chunks, making use of parallel computation even when training sequential models on long series (using truncated backpropagation).

To get started, take a look at the examples/ directory, which includes:

Making probabilistic forecasts (examples/predict.py)
Using exogenous features to train on data with known anomalies/changepoints (examples/known_anomaly.py)
Learning correlations between series (multivariate forecasting/anomaly detection; examples/multivariate.py)
More advanced custom model building (examples/lstm.py)
TFTS includes many other modeling tools, including non-linear autoregression (see the hidden_layer_sizes argument to ARRegressor in estimators.py) and a collection of components for linear state space modeling (level, trend, period, vector autoregression, moving averages; see the StructuralEnsembleRegressor in estimators.py). Both model classes support heuristics for ignoring un-labeled anomalies in training data. Trained models can be exported for inference/serving in SavedModel format (see examples/multivariate.py).


要开始,请查看examples /目录,其中包括:

进行概率预测(examples / predict.py)
使用外部特征来训练具有已知异常/变化点的数据(examples / known_anomaly.py)
学习系列之间的相关性(多变量预测/异常检测;示例/ multivariate.py)
更高级的自定义模型构建(examples / lstm.py)
TFTS​​包括许多其他的建模工具,包括非线性自回归(参见estimators.py中的ARRegressor的hidden_​​layer_sizes参数)以及线性状态空间建模(级别,趋势,周期,向量自回归,移动平均值)的组件集合;请参阅StructuralEnsembleRegressor在estimateators.py中)。两个模型类都支持启发式,用于忽略训练数据中未标记的异常。受过训练的模型可以以SavedModel格式导出推理/服务(参见examples / multivariate.py)。


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