了解如何使用TSFERSH-库(0.4.0版)预测特定系列的下一个N值有一些问题。下面是我的代码:
# load data train/test datasets
train, Y, test, YY = prepare_train_test()
# add series ID
train['TS_ID'] = pd.Categorical(train['QTR_HR_START']).codes
test['TS_ID'] = pd.Categorical(test['QTR_HR_START']).codes
# add ordered id for concrete event of series
for id in sorted(train['TS_ID'].unique()):
train.ix[train.TS_ID == id, 'TIME_ORDER_ID'] = pd.Categorical(train[train.TS_ID == id]['DATETIME']).codes
for id in sorted(test['TS_ID'].unique()):
test.ix[test.TS_ID == id, 'TIME_ORDER_ID'] = pd.Categorical(test[test.TS_ID == id]['DATETIME']).codes
# perform feature extraction for my signal
extraction_settings = FeatureExtractionSettings()
extraction_settings.IMPUTE = impute # Fill in Infs and NaNs
X = extract_features(train, column_id='TS_ID', feature_extraction_settings=extraction_settings).values
XT = extract_features(test, column_id='TS_ID', feature_extraction_settings=extraction_settings).values
# there should be as example
# model = xgb.DMatrix(X, label=Y, missing=np.nan)
# model.fit()
# model.predict(XT)
但是,在X = extract_features(...)
行之后,我在调试器上看到以下结果
这意味着最初的X-dataset/features
(shape=,722,10)被转化为形状(80,1899年)。
“80”从哪里来?我想是train.TS_ID
来的。但是我的XT
-dataset仍然包含722行(9天*每天80个不同的系列)。
那么,我怎么能提前9天预测呢?还是只有下一阶段的预测?
发布于 2017-09-08 09:04:33
TSFRESH已经在支持时间序列预测。
参见详细信息和示例这里和这里
https://datascience.stackexchange.com/questions/15075
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