How to use future_time_series and finetune_forecast_model?

[gabriel-joy]

Whan I try to introduce known data from the future in the forecast I get higher values, I guess I miss something, Please advise.

future_data = pd.read_csv(
"~/Downloads/ebstestmures-futures-20241024.csv",
parse_dates=[timestamp_column],
)

pipeline = TimeSeriesForecastingPipeline(
model=finetune_forecast_model,
timestamp_column=timestamp_column,
target_columns=["far_ea"],
observable_columns=["DayIndex","Hour","temperature","prod_ea"],
future_time_series=future_data,
explode_forecasts=True,
inverse_scale_outputs=True,
freq="1h",
id_columns=[],
device="cuda"
)

fewshots_forecast = pipeline(data.iloc[-context_length:].copy())

without future_time_series data are in the range:

with future_time_series data included values are way too high:

[wgifford]

How was "finetune_forecast_model" finetuned?

[gabriel-joy]

I just followed the exogen tutorial

column_specifiers = {
    "timestamp_column": timestamp_column,
    "id_columns": id_columns,
    "target_columns": ["far_ea"],
    "control_columns": [
        "DayIndex",
        "Hour",
        "temperature",
        "prod_ea"
    ],
}

split_params = {"train": [0, 0.5], "valid": [0.5, 0.75], "test": [0.75, 1.0]}

tsp = TimeSeriesPreprocessor(
    **column_specifiers,
    context_length=context_length,
    prediction_length=forecast_length,
    scaling=True,
    encode_categorical=False,
    scaler_type="standard",
)

train_dataset, valid_dataset, test_dataset = get_datasets(
    tsp,
    data,
    split_params,
)

train_dataset[3]

finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(
    "ibm-granite/granite-timeseries-ttm-v1",
    revision=TTM_MODEL_REVISION,
    num_input_channels=tsp.num_input_channels,
    decoder_mode="mix_channel",  # exog:  set to mix_channel for mixing channels in history
    prediction_channel_indices=tsp.prediction_channel_indices,
    exogenous_channel_indices=tsp.exogenous_channel_indices,
    fcm_context_length=1,  # exog: indicates lag length to use in the exog fusion. for Ex. if today sales can get affected by discount on +/- 2 days, mention 2
    fcm_use_mixer=True,  # exog: Try true (1st option) or false
    fcm_mix_layers=2,  # exog: Number of layers for exog mixing
    enable_forecast_channel_mixing=True,  # exog: set true for exog mixing
    fcm_prepend_past=True,  # exog: set true to include lag from history during exog infusion.
)
finetune_forecast_model

print(
    "Number of params before freezing backbone",
    count_parameters(finetune_forecast_model),
)

# Freeze the backbone of the model
for param in finetune_forecast_model.backbone.parameters():
    param.requires_grad = False

# Count params
print(
    "Number of params after freezing the backbone",
    count_parameters(finetune_forecast_model),
)

# Important parameters
learning_rate = 0.000298364724028334
num_epochs = 50  # Ideally, we need more epochs (try offline preferably in a gpu for faster computation)
batch_size = 64

print(f"Using learning rate = {learning_rate}")
finetune_forecast_args = TrainingArguments(
    output_dir=os.path.join(OUT_DIR, "output"),
    overwrite_output_dir=True,
    learning_rate=learning_rate,
    num_train_epochs=num_epochs,
    do_eval=True,
    evaluation_strategy="epoch",
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    dataloader_num_workers=8,
    report_to=None,
    save_strategy="epoch",
    logging_strategy="epoch",
    save_total_limit=1,
    logging_dir=os.path.join(OUT_DIR, "logs"),  # Make sure to specify a logging directory
    load_best_model_at_end=True,  # Load the best model when training ends
    metric_for_best_model="eval_loss",  # Metric to monitor for early stopping
    greater_is_better=False,  # For loss
)

# Create the early stopping callback
early_stopping_callback = EarlyStoppingCallback(
    early_stopping_patience=10,  # Number of epochs with no improvement after which to stop
    early_stopping_threshold=0.0,  # Minimum improvement required to consider as improvement
)
tracking_callback = TrackingCallback()

# Optimizer and scheduler
optimizer = AdamW(finetune_forecast_model.parameters(), lr=learning_rate)
scheduler = OneCycleLR(
    optimizer,
    learning_rate,
    epochs=num_epochs,
    steps_per_epoch=math.ceil(len(train_dataset) / (batch_size)),
)

finetune_forecast_trainer = Trainer(
    model=finetune_forecast_model,
    args=finetune_forecast_args,
    train_dataset=train_dataset,
    eval_dataset=valid_dataset,
    callbacks=[early_stopping_callback, tracking_callback],
    optimizers=(optimizer, scheduler),
)

# Fine tune
finetune_forecast_trainer.train()

[wgifford]

@gabriel-joy Just checking in, were you able to resolve the issue above? There have been some changes in the behavior in the forecasting pipeline and I would like to see if they resolve the issue. You can now pass the preprocessor to the forecasting pipeline and it will manage the scaling/inverse scaling appropriately.