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203 lines
12 KiB
Markdown
203 lines
12 KiB
Markdown
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# AutoML - Time Series Forecast
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### Prerequisites
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Install the [ts_forecast] option.
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```bash
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pip install "flaml[ts_forecast]"
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```
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### Univariate time series
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```python
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import numpy as np
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from flaml import AutoML
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X_train = np.arange('2014-01', '2021-01', dtype='datetime64[M]')
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y_train = np.random.random(size=72)
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automl = AutoML()
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automl.fit(X_train=X_train[:72], # a single column of timestamp
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y_train=y_train, # value for each timestamp
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period=12, # time horizon to forecast, e.g., 12 months
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task='ts_forecast', time_budget=15, # time budget in seconds
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log_file_name="ts_forecast.log",
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)
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print(automl.predict(X_train[72:]))
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```
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#### Sample output
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```
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[flaml.automl: 11-15 18:44:49] {1485} INFO - Data split method: time
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INFO:flaml.automl:Data split method: time
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[flaml.automl: 11-15 18:44:49] {1489} INFO - Evaluation method: cv
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INFO:flaml.automl:Evaluation method: cv
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[flaml.automl: 11-15 18:44:49] {1540} INFO - Minimizing error metric: mape
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INFO:flaml.automl:Minimizing error metric: mape
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[flaml.automl: 11-15 18:44:49] {1577} INFO - List of ML learners in AutoML Run: ['prophet', 'arima', 'sarimax']
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INFO:flaml.automl:List of ML learners in AutoML Run: ['prophet', 'arima', 'sarimax']
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[flaml.automl: 11-15 18:44:49] {1826} INFO - iteration 0, current learner prophet
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INFO:flaml.automl:iteration 0, current learner prophet
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[flaml.automl: 11-15 18:45:00] {1944} INFO - Estimated sufficient time budget=104159s. Estimated necessary time budget=104s.
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INFO:flaml.automl:Estimated sufficient time budget=104159s. Estimated necessary time budget=104s.
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[flaml.automl: 11-15 18:45:00] {2029} INFO - at 10.5s, estimator prophet's best error=1.5681, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 10.5s, estimator prophet's best error=1.5681, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:00] {1826} INFO - iteration 1, current learner arima
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INFO:flaml.automl:iteration 1, current learner arima
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[flaml.automl: 11-15 18:45:00] {2029} INFO - at 10.7s, estimator arima's best error=2.3515, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 10.7s, estimator arima's best error=2.3515, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:00] {1826} INFO - iteration 2, current learner arima
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INFO:flaml.automl:iteration 2, current learner arima
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[flaml.automl: 11-15 18:45:01] {2029} INFO - at 11.5s, estimator arima's best error=2.1774, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 11.5s, estimator arima's best error=2.1774, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:01] {1826} INFO - iteration 3, current learner arima
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INFO:flaml.automl:iteration 3, current learner arima
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[flaml.automl: 11-15 18:45:01] {2029} INFO - at 11.9s, estimator arima's best error=2.1774, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 11.9s, estimator arima's best error=2.1774, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:01] {1826} INFO - iteration 4, current learner arima
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INFO:flaml.automl:iteration 4, current learner arima
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[flaml.automl: 11-15 18:45:02] {2029} INFO - at 12.9s, estimator arima's best error=1.8560, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 12.9s, estimator arima's best error=1.8560, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:02] {1826} INFO - iteration 5, current learner arima
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INFO:flaml.automl:iteration 5, current learner arima
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[flaml.automl: 11-15 18:45:04] {2029} INFO - at 14.4s, estimator arima's best error=1.8560, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 14.4s, estimator arima's best error=1.8560, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:04] {1826} INFO - iteration 6, current learner sarimax
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INFO:flaml.automl:iteration 6, current learner sarimax
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[flaml.automl: 11-15 18:45:04] {2029} INFO - at 14.7s, estimator sarimax's best error=2.3515, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 14.7s, estimator sarimax's best error=2.3515, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:04] {1826} INFO - iteration 7, current learner sarimax
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INFO:flaml.automl:iteration 7, current learner sarimax
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[flaml.automl: 11-15 18:45:04] {2029} INFO - at 15.0s, estimator sarimax's best error=1.6371, best estimator prophet's best error=1.5681
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INFO:flaml.automl: at 15.0s, estimator sarimax's best error=1.6371, best estimator prophet's best error=1.5681
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[flaml.automl: 11-15 18:45:05] {2242} INFO - retrain prophet for 0.5s
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INFO:flaml.automl:retrain prophet for 0.5s
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[flaml.automl: 11-15 18:45:05] {2247} INFO - retrained model: <prophet.forecaster.Prophet object at 0x7f042ba1da50>
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INFO:flaml.automl:retrained model: <prophet.forecaster.Prophet object at 0x7f042ba1da50>
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[flaml.automl: 11-15 18:45:05] {1608} INFO - fit succeeded
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INFO:flaml.automl:fit succeeded
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[flaml.automl: 11-15 18:45:05] {1610} INFO - Time taken to find the best model: 10.450132608413696
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INFO:flaml.automl:Time taken to find the best model: 10.450132608413696
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0 0.384715
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1 0.191349
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2 0.372324
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3 0.814549
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4 0.269616
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5 0.470667
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6 0.603665
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7 0.256773
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8 0.408787
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9 0.663065
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10 0.619943
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11 0.090284
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Name: yhat, dtype: float64
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```
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### Multivariate time series
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```python
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import statsmodels.api as sm
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data = sm.datasets.co2.load_pandas().data
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# data is given in weeks, but the task is to predict monthly, so use monthly averages instead
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data = data['co2'].resample('MS').mean()
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data = data.fillna(data.bfill()) # makes sure there are no missing values
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data = data.to_frame().reset_index()
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num_samples = data.shape[0]
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time_horizon = 12
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split_idx = num_samples - time_horizon
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train_df = data[:split_idx] # train_df is a dataframe with two columns: timestamp and label
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X_test = data[split_idx:]['index'].to_frame() # X_test is a dataframe with dates for prediction
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y_test = data[split_idx:]['co2'] # y_test is a series of the values corresponding to the dates for prediction
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from flaml import AutoML
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automl = AutoML()
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settings = {
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"time_budget": 10, # total running time in seconds
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"metric": 'mape', # primary metric for validation: 'mape' is generally used for forecast tasks
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"task": 'ts_forecast', # task type
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"log_file_name": 'CO2_forecast.log', # flaml log file
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"eval_method": "holdout", # validation method can be chosen from ['auto', 'holdout', 'cv']
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"seed": 7654321, # random seed
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}
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automl.fit(dataframe=train_df, # training data
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label='co2', # label column
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period=time_horizon, # key word argument 'period' must be included for forecast task)
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**settings)
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```
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#### Sample output
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```
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[flaml.automl: 11-15 18:54:12] {1485} INFO - Data split method: time
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INFO:flaml.automl:Data split method: time
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[flaml.automl: 11-15 18:54:12] {1489} INFO - Evaluation method: holdout
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INFO:flaml.automl:Evaluation method: holdout
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[flaml.automl: 11-15 18:54:13] {1540} INFO - Minimizing error metric: mape
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INFO:flaml.automl:Minimizing error metric: mape
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[flaml.automl: 11-15 18:54:13] {1577} INFO - List of ML learners in AutoML Run: ['prophet', 'arima', 'sarimax']
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INFO:flaml.automl:List of ML learners in AutoML Run: ['prophet', 'arima', 'sarimax']
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[flaml.automl: 11-15 18:54:13] {1826} INFO - iteration 0, current learner prophet
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INFO:flaml.automl:iteration 0, current learner prophet
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[flaml.automl: 11-15 18:54:15] {1944} INFO - Estimated sufficient time budget=25297s. Estimated necessary time budget=25s.
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INFO:flaml.automl:Estimated sufficient time budget=25297s. Estimated necessary time budget=25s.
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[flaml.automl: 11-15 18:54:15] {2029} INFO - at 2.6s, estimator prophet's best error=0.0008, best estimator prophet's best error=0.0008
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INFO:flaml.automl: at 2.6s, estimator prophet's best error=0.0008, best estimator prophet's best error=0.0008
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[flaml.automl: 11-15 18:54:15] {1826} INFO - iteration 1, current learner prophet
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INFO:flaml.automl:iteration 1, current learner prophet
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[flaml.automl: 11-15 18:54:18] {2029} INFO - at 5.2s, estimator prophet's best error=0.0008, best estimator prophet's best error=0.0008
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INFO:flaml.automl: at 5.2s, estimator prophet's best error=0.0008, best estimator prophet's best error=0.0008
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[flaml.automl: 11-15 18:54:18] {1826} INFO - iteration 2, current learner arima
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INFO:flaml.automl:iteration 2, current learner arima
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[flaml.automl: 11-15 18:54:18] {2029} INFO - at 5.5s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0008
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INFO:flaml.automl: at 5.5s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0008
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[flaml.automl: 11-15 18:54:18] {1826} INFO - iteration 3, current learner arima
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INFO:flaml.automl:iteration 3, current learner arima
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[flaml.automl: 11-15 18:54:18] {2029} INFO - at 5.6s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0008
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INFO:flaml.automl: at 5.6s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0008
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[flaml.automl: 11-15 18:54:18] {1826} INFO - iteration 4, current learner prophet
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INFO:flaml.automl:iteration 4, current learner prophet
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[flaml.automl: 11-15 18:54:21] {2029} INFO - at 8.1s, estimator prophet's best error=0.0005, best estimator prophet's best error=0.0005
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INFO:flaml.automl: at 8.1s, estimator prophet's best error=0.0005, best estimator prophet's best error=0.0005
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[flaml.automl: 11-15 18:54:21] {1826} INFO - iteration 5, current learner arima
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INFO:flaml.automl:iteration 5, current learner arima
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[flaml.automl: 11-15 18:54:21] {2029} INFO - at 8.9s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0005
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INFO:flaml.automl: at 8.9s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0005
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[flaml.automl: 11-15 18:54:21] {1826} INFO - iteration 6, current learner arima
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INFO:flaml.automl:iteration 6, current learner arima
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[flaml.automl: 11-15 18:54:22] {2029} INFO - at 9.7s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0005
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INFO:flaml.automl: at 9.7s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0005
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[flaml.automl: 11-15 18:54:22] {1826} INFO - iteration 7, current learner sarimax
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INFO:flaml.automl:iteration 7, current learner sarimax
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[flaml.automl: 11-15 18:54:23] {2029} INFO - at 10.1s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0005
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INFO:flaml.automl: at 10.1s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0005
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[flaml.automl: 11-15 18:54:23] {2242} INFO - retrain prophet for 0.9s
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INFO:flaml.automl:retrain prophet for 0.9s
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[flaml.automl: 11-15 18:54:23] {2247} INFO - retrained model: <prophet.forecaster.Prophet object at 0x7f0418e21f50>
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INFO:flaml.automl:retrained model: <prophet.forecaster.Prophet object at 0x7f0418e21f50>
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[flaml.automl: 11-15 18:54:23] {1608} INFO - fit succeeded
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INFO:flaml.automl:fit succeeded
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[flaml.automl: 11-15 18:54:23] {1610} INFO - Time taken to find the best model: 8.118467330932617
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INFO:flaml.automl:Time taken to find the best model: 8.118467330932617
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[flaml.automl: 11-15 18:54:23] {1624} WARNING - Time taken to find the best model is 81% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.
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WARNING:flaml.automl:Time taken to find the best model is 81% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.
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```
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#### Compute and plot predictions
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```python
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flaml_y_pred = automl.predict(X_test)
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import matplotlib.pyplot as plt
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plt.plot(X_test, y_test, label='Actual level')
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plt.plot(X_test, flaml_y_pred, label='FLAML forecast')
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plt.xlabel('Date')
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plt.ylabel('CO2 Levels')
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plt.legend()
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```
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[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb)
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