Have some issue with understanding how to use TSFERSH-library (version 0.4.0) to forecast next N-values of particular series. Below my code:
# 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)
It's mean that initial
X-dataset/features (shape=(722,10) were transformed to shape (80, 1899).
Where does '80' come from? I guess from
train.TS_ID comes. But my
XT-dataset still contains 722-rows (9 days * 80 different series per day).
So, how can I predict for 9 days in advance? or is there only forecast for next period?