gluonts

from gluonts.dataset.common import ListDataset from gluonts.torch.model.simple_feedforward import SimpleFeedForwardEstimator from pytorch_lightning import Trainer from gluonts.evaluation import make_evaluation_predictions, Evaluator import pandas as pd import numpy as np # 1. Prepare Data target_data = np.random.rand(100) # Dummy time series data start_date = pd.Timestamp("2023-01-01", freq="H") data_entry = { "start": start_date, "target": target_data, "item_id": "item_A" } training_data = ListDataset([data_entry], freq="H") # For evaluation, we need to split data into past and future # In a real scenario, you'd have separate test data or perform backtesting prediction_length = 24 full_data_entry = { "start": pd.Timestamp("2023-01-01", freq="H"), "target": np.concatenate([np.random.rand(80), np.random.rand(24)]), # 80 for training, 24 for future "item_id": "item_B" } # Simulate a test dataset by cutting off the prediction_length from the full series test_data = ListDataset([{ "start": full_data_entry["start"], "target": full_data_entry["target"][:-prediction_length], "feat_static_cat": [0], "item_id": full_data_entry["item_id"] }], freq="H") # 2. Define Estimator estimator = SimpleFeedForwardEstimator( prediction_length=prediction_length, context_length=prediction_length * 2, trainer=Trainer(max_epochs=5, enable_checkpointing=False, enable_progress_bar=False, logger=False), num_hidden_dimensions=[10, 10] ) # 3. Train the model predictor = estimator.train(training_data=training_data) # 4. Make Predictions forecast_it, ts_it = make_evaluation_predictions( dataset=test_data, predictor=predictor, num_samples=100 ) forecasts = list(forecast_it) ts = list(ts_it) # 5. Evaluate evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9]) agg_metrics, item_metrics = evaluator(ts, forecasts, num_series=len(test_data)) print("Aggregated metrics:", agg_metrics) print("First forecast (mean):") print(forecasts[0].mean)