Significant Difference Between Batched Prediction and Looped Prediction in timesfm

[volcanomao]

Hi everyone,

I’m working on a time series forecasting problem where I use a function forecast_with_covariates that accepts various covariates and input data. I’ve encountered a puzzling issue: the predictions differ significantly when I feed the data in batches versus when I process each batch individually in a loop.

Here’s what I’ve tried:

1.	Batched Prediction: I passed the data in batches to the model, and here’s the output shape and first few values:
•	Test forecast shape: (2, 20)
•	First batch test forecast first 5 values: [0.7577893, 1.023695, 0.91585237, 0.89688027, 0.65517604]
•	Second batch test forecast first 5 values: [0.20441657, 0.2393372, -0.22837491, -0.38262987, -0.5851944]
2.	Looped Prediction: I passed the data one batch at a time in a loop. The results are significantly different:
•	Looped forecast results shape: (2, 20)
•	First batch looped forecast first 5 values: [0.6246872, 0.92845935, 0.92428553, 1.041631, 0.36359525]
•	Second batch looped forecast first 5 values: [0.14961839, 0.43704915, -0.04414537, -0.20000435, -0.66028154]

Could anyone help explain why there is such a noticeable difference between these two approaches? Is there a known issue with batching, or could it be related to the covariates handling in the model?

# Simple test case
import numpy as np

# Create test data
forecast_input = [
    np.sin(np.linspace(0, 20, 100)),
    np.cos(np.linspace(0, 20, 100))
]
dynamic_numerical_covariates = {
    "temperature": [np.random.rand(120), np.random.rand(120)]
}
dynamic_categorical_covariates = {
    "weekday": [np.random.randint(0, 7, 120), np.random.randint(0, 7, 120)]
}
static_numerical_covariates = {
    "base_price": [10.5, 15.0]
}
static_categorical_covariates = {
    "category": ["food", "beverage"]
}

# Call the forecast_with_covariates function
test_forecast, _ = model.forecast_with_covariates(
    forecast_input,
    dynamic_numerical_covariates=dynamic_numerical_covariates,
    dynamic_categorical_covariates=dynamic_categorical_covariates,
    static_numerical_covariates=static_numerical_covariates,
    static_categorical_covariates=static_categorical_covariates,
    freq=[0, 0],
    xreg_mode="xreg + timesfm",
    ridge=0.0,
    force_on_cpu=False,
    normalize_xreg_target_per_input=True
)

test_forecast = np.array(test_forecast)  # Convert list to NumPy array

print("Test forecast shape:", test_forecast.shape)
print("First batch test forecast first 5 values:", test_forecast[0][:5])
print("Second batch test forecast first 5 values:", test_forecast[1][:5])


# Use a loop to call forecast_with_covariates for each batch individually
num_batches = len(forecast_input)
single_batch_forecast_list = []

for i in range(num_batches):
    single_batch_forecast, _ = model.forecast_with_covariates(
        [forecast_input[i]],  # Input one batch at a time
        dynamic_numerical_covariates={k: [v[i]] for k, v in dynamic_numerical_covariates.items()},
        dynamic_categorical_covariates={k: [v[i]] for k, v in dynamic_categorical_covariates.items()},
        static_numerical_covariates={k: [v[i]] for k, v in static_numerical_covariates.items()},
        static_categorical_covariates={k: [v[i]] for k, v in static_categorical_covariates.items()},
        freq=[0],  # Input one frequency at a time
        xreg_mode="xreg + timesfm",
        ridge=0.0,
        force_on_cpu=False,
        normalize_xreg_target_per_input=True
    )
    single_batch_forecast_list.append(single_batch_forecast[0])  # Add forecast result to the list

# Combine all batch forecast results
looped_forecast_results = np.array(single_batch_forecast_list)

print("Looped forecast results shape:", looped_forecast_results.shape)
print("First batch looped forecast first 5 values:", looped_forecast_results[0][:5])
print("Second batch looped forecast first 5 values:", looped_forecast_results[1][:5])

# Compare looped forecast results with the original forecast results
print("\nDifferences between looped forecast results and original forecast results:")
print("Maximum absolute error:", np.max(np.abs(looped_forecast_results - test_forecast)))
print("Mean absolute error:", np.mean(np.abs(looped_forecast_results - test_forecast)))

Output:

Test forecast shape: (2, 20)
First batch test forecast first 5 values: [0.7577893  1.023695   0.91585237 0.89688027 0.65517604]
Second batch test forecast first 5 values: [ 0.20441657  0.2393372  -0.22837491 -0.38262987 -0.5851944 ]
Looped forecast results shape: (2, 20)
First batch looped forecast first 5 values: [0.6246872  0.92845935 0.92428553 1.041631   0.36359525]
Second batch looped forecast first 5 values: [ 0.14961839  0.43704915 -0.04414537 -0.20000435 -0.66028154]

Differences between looped forecast results and original forecast results:
Maximum absolute error: 0.36772624
Mean absolute error: 0.14028119

[siriuz42]

It's caused by the covariate handling. What the function does under the hood is that it will fit a linear model on the time points of the whole batch. It is WAI that you'll see different results if the batching is different.