# AutoML - Time Series Forecast ### Prerequisites Install the [ts_forecast] option. ```bash pip install "flaml[ts_forecast]" ``` ### Univariate time series ```python import numpy as np from flaml import AutoML X_train = np.arange('2014-01', '2022-01', dtype='datetime64[M]') y_train = np.random.random(size=84) automl = AutoML() automl.fit(X_train=X_train[:84], # a single column of timestamp y_train=y_train, # value for each timestamp period=12, # time horizon to forecast, e.g., 12 months task='ts_forecast', time_budget=15, # time budget in seconds log_file_name="ts_forecast.log", eval_method="holdout", ) print(automl.predict(X_train[84:])) ``` #### Sample output ```python [flaml.automl: 01-21 08:01:20] {2018} INFO - task = ts_forecast [flaml.automl: 01-21 08:01:20] {2020} INFO - Data split method: time [flaml.automl: 01-21 08:01:20] {2024} INFO - Evaluation method: holdout [flaml.automl: 01-21 08:01:20] {2124} INFO - Minimizing error metric: mape [flaml.automl: 01-21 08:01:21] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'] [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 0, current learner lgbm [flaml.automl: 01-21 08:01:21] {2547} INFO - Estimated sufficient time budget=1429s. Estimated necessary time budget=1s. [flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 1, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 2, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 3, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 4, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 5, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 6, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9652, best estimator lgbm's best error=0.9652 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 7, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 8, current learner lgbm [flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466 [flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 9, current learner lgbm [flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466 [flaml.automl: 01-21 08:01:22] {2434} INFO - 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iteration 79, current learner xgb_limitdepth [flaml.automl: 01-21 08:01:26] {2594} INFO - at 5.1s, estimator xgb_limitdepth's best error=0.9683, best estimator arima's best error=0.5693 [flaml.automl: 01-21 08:01:26] {2434} INFO - iteration 80, current learner xgb_limitdepth [flaml.automl: 01-21 08:01:26] {2594} INFO - at 5.1s, estimator xgb_limitdepth's best error=0.9683, best estimator arima's best error=0.5693 [flaml.automl: 01-21 08:01:26] {2434} INFO - iteration 81, current learner sarimax [flaml.automl: 01-21 08:01:26] {2594} INFO - at 5.1s, estimator sarimax's best error=0.5693, best estimator arima's best error=0.5693 [flaml.automl: 01-21 08:01:26] {2434} INFO - iteration 82, current learner prophet [flaml.automl: 01-21 08:01:27] {2594} INFO - at 6.6s, estimator prophet's best error=1.4076, best estimator arima's best error=0.5693 [flaml.automl: 01-21 08:01:27] {2434} INFO - iteration 83, current learner xgb_limitdepth [flaml.automl: 01-21 08:01:27] {2594} INFO - at 6.6s, estimator xgb_limitdepth's best error=0.9683, best estimator arima's best error=0.5693 [flaml.automl: 01-21 08:01:27] {2434} INFO - 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iteration 94, current learner xgb_limitdepth [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.1s, estimator xgb_limitdepth's best error=0.9683, best estimator sarimax's best error=0.5600 [flaml.automl: 01-21 08:01:28] {2434} INFO - iteration 95, current learner sarimax [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.2s, estimator sarimax's best error=0.5600, best estimator sarimax's best error=0.5600 [flaml.automl: 01-21 08:01:28] {2434} INFO - iteration 96, current learner arima [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.2s, estimator arima's best error=0.5693, best estimator sarimax's best error=0.5600 [flaml.automl: 01-21 08:01:28] {2434} INFO - iteration 97, current learner arima [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.2s, estimator arima's best error=0.5693, best estimator sarimax's best error=0.5600 [flaml.automl: 01-21 08:01:28] {2434} INFO - iteration 98, current learner extra_tree [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.3s, estimator extra_tree's best error=0.9499, best estimator sarimax's best error=0.5600 [flaml.automl: 01-21 08:01:28] {2434} INFO - iteration 99, current learner sarimax [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.3s, estimator sarimax's best error=0.5600, best estimator sarimax's best error=0.5600 [flaml.automl: 01-21 08:01:28] {2434} INFO - iteration 100, current learner xgb_limitdepth [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.3s, estimator xgb_limitdepth's best error=0.9683, best estimator sarimax's best error=0.5600 ``` ### Multivariate time series ```python import statsmodels.api as sm data = sm.datasets.co2.load_pandas().data # data is given in weeks, but the task is to predict monthly, so use monthly averages instead data = data['co2'].resample('MS').mean() data = data.fillna(data.bfill()) # makes sure there are no missing values data = data.to_frame().reset_index() num_samples = data.shape[0] time_horizon = 12 split_idx = num_samples - time_horizon train_df = data[:split_idx] # train_df is a dataframe with two columns: timestamp and label X_test = data[split_idx:]['index'].to_frame() # X_test is a dataframe with dates for prediction y_test = data[split_idx:]['co2'] # y_test is a series of the values corresponding to the dates for prediction from flaml import AutoML automl = AutoML() settings = { "time_budget": 10, # total running time in seconds "metric": 'mape', # primary metric for validation: 'mape' is generally used for forecast tasks "task": 'ts_forecast', # task type "log_file_name": 'CO2_forecast.log', # flaml log file "eval_method": "holdout", # validation method can be chosen from ['auto', 'holdout', 'cv'] "seed": 7654321, # random seed } automl.fit(dataframe=train_df, # training data label='co2', # label column period=time_horizon, # key word argument 'period' must be included for forecast task) **settings) ``` #### Sample output ``` [flaml.automl: 01-21 07:54:04] {2018} INFO - task = ts_forecast [flaml.automl: 01-21 07:54:04] {2020} INFO - Data split method: time [flaml.automl: 01-21 07:54:04] {2024} INFO - Evaluation method: holdout [flaml.automl: 01-21 07:54:04] {2124} INFO - Minimizing error metric: mape Importing plotly failed. Interactive plots will not work. [flaml.automl: 01-21 07:54:04] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax'] [flaml.automl: 01-21 07:54:04] {2434} INFO - iteration 0, current learner lgbm [flaml.automl: 01-21 07:54:05] {2547} INFO - Estimated sufficient time budget=2145s. Estimated necessary time budget=2s. [flaml.automl: 01-21 07:54:05] {2594} INFO - at 0.9s, estimator lgbm's best error=0.0621, best estimator lgbm's best error=0.0621 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 1, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.0s, estimator lgbm's best error=0.0574, best estimator lgbm's best error=0.0574 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 2, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.0s, estimator lgbm's best error=0.0464, best estimator lgbm's best error=0.0464 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 3, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.0s, estimator lgbm's best error=0.0464, best estimator lgbm's best error=0.0464 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 4, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.0s, estimator lgbm's best error=0.0365, best estimator lgbm's best error=0.0365 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 5, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.1s, estimator lgbm's best error=0.0192, best estimator lgbm's best error=0.0192 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 6, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.1s, estimator lgbm's best error=0.0192, best estimator lgbm's best error=0.0192 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 7, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.1s, estimator lgbm's best error=0.0192, best estimator lgbm's best error=0.0192 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 8, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.2s, estimator lgbm's best error=0.0110, best estimator lgbm's best error=0.0110 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 9, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.2s, estimator lgbm's best error=0.0110, best estimator lgbm's best error=0.0110 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 10, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.2s, estimator lgbm's best error=0.0036, best estimator lgbm's best error=0.0036 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 11, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.4s, estimator lgbm's best error=0.0023, best estimator lgbm's best error=0.0023 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 12, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.4s, estimator lgbm's best error=0.0023, best estimator lgbm's best error=0.0023 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 13, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.5s, estimator lgbm's best error=0.0021, best estimator lgbm's best error=0.0021 [flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 14, current learner lgbm [flaml.automl: 01-21 07:54:05] {2594} INFO - at 1.6s, estimator lgbm's best error=0.0021, best estimator lgbm's best error=0.0021 [flaml.automl: 01-21 07:54:05] {2434} INFO - 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iteration 44, current learner arima [flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.1s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0008 [flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 45, current learner sarimax [flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.4s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0008 [flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 46, current learner lgbm [flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.5s, estimator lgbm's best error=0.0017, best estimator prophet's best error=0.0008 [flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 47, current learner sarimax [flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.6s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0008 [flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 48, current learner sarimax [flaml.automl: 01-21 07:54:11] {2594} INFO - at 6.9s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0008 [flaml.automl: 01-21 07:54:11] {2434} INFO - 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retrain prophet for 0.6s [flaml.automl: 01-21 07:54:14] {2831} INFO - retrained model: [flaml.automl: 01-21 07:54:14] {2210} INFO - fit succeeded [flaml.automl: 01-21 07:54:14] {2211} INFO - Time taken to find the best model: 9.339771270751953 [flaml.automl: 01-21 07:54:14] {2222} WARNING - Time taken to find the best model is 93% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget. ``` #### Compute and plot predictions The example plotting code requires matplotlib. ```python flaml_y_pred = automl.predict(X_test) import matplotlib.pyplot as plt plt.plot(X_test, y_test, label='Actual level') plt.plot(X_test, flaml_y_pred, label='FLAML forecast') plt.xlabel('Date') plt.ylabel('CO2 Levels') plt.legend() ``` ![png](images/CO2.png) [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)