Time Series Model Training: Prophet, NeuralProphet, TimesFM

5+ years in AI/ML | 30+ forecasting projects | 500+ trained models. Backed by proven experience, we guarantee robust forecasting.

We develop and tune time series forecasting models — Prophet, NeuralProphet, and TimesFM — to help plan sales, purchases, and capacity utilization. Recently, one of our clients — a retail chain with 500 stores — faced unstable weekly sales forecasts: Seasonal Naive gave SMAPE of 15%, but the business required 10% accuracy. We chose Prophet From Wikipedia, the free encyclopedia, tuned changepoint_prior_scale=0.05, seasonality_mode='multiplicative', and achieved SMAPE of 8% — halving the error. This improvement saved the client $200,000 annually in inventory costs. This is a real example of how the right model and tuning solve the problem.

We have been in AI/ML for over 5 years and completed 30+ forecasting projects. Our approach includes strict cross-validation and comparison with multiple baselines. We publish model cards and ensure experiment reproducibility. Training projects typically start from $5,000 for a single model with cross-validation, up to $20,000 for a full production pipeline including monitoring and retraining. The budget is determined after analyzing your data — cost depends on data complexity and target accuracy.

What criteria should you use to select a model?

Choosing the right model depends on three factors: data volume, need for interpretability, and presence of external events (holidays, promotions). If you have little data (less than 2 years) and need transparency — pick Prophet. If there are nonlinear lags and external regressors — NeuralProphet. If you have abundant data (thousands of series) and need maximum accuracy without explanation — TimesFM. Contact us for a tailored recommendation based on your specific case.

What's included in end-to-end model training?

We prepare data, tune hyperparameters, perform cross-validation, build a production pipeline (Airflow + MLflow), document the model, and deliver an API endpoint. You get a trained model, documentation, an operational manual, and one month of support. Additionally, we set up data drift monitoring and automatic model retraining.

Prophet: Decomposition Model

Meta Prophet is an additive model:

y(t) = trend(t) + seasonality(t) + holidays(t) + ε(t) 

Trend is piecewise linear or logistic growth. Changepoints — automatic detection of trend change points via L1 regularization.

Seasonality is described by Fourier series: yearly with N=10 (default), weekly with N=3, custom for any period.

Training and tuning:

from prophet import Prophet import pandas as pd m = Prophet( changepoint_prior_scale=0.05, # trend flexibility seasonality_prior_scale=10.0, # seasonality flexibility holidays_prior_scale=10.0, seasonality_mode='multiplicative' # for data with growth ) m.add_country_holidays(country_name='RU') m.add_seasonality(name='monthly', period=30.5, fourier_order=5) m.fit(df) # df with columns ds, y 

Key tuning parameters: changepoint_prior_scale (0.001–0.5) controls overfitting to trend, seasonality_mode ('additive' for stationary, 'multiplicative' for growing), fourier_order (higher = more flexible seasonality).

Prophet cross-validation:

from prophet.diagnostics import cross_validation, performance_metrics df_cv = cross_validation(m, initial='730 days', period='180 days', horizon='365 days') df_p = performance_metrics(df_cv) 

NeuralProphet: Prophet with Neural Components

NeuralProphet extends Prophet with autoregression (AR-Net) and nonlinear lagged regressors. Training via PyTorch is significantly faster than MCMC-Prophet.

from neuralprophet import NeuralProphet m = NeuralProphet( n_forecasts=7, # forecast horizon n_lags=14, # number of lags for AR seasonality_mode='auto', learning_rate=0.01 ) m = m.add_country_holidays('RU') metrics = m.fit(df, freq='D', validation_df=df_val) 

NeuralProphet is effective when nonlinear lag dependencies, multiple forecast steps, and external regressors with lag are present.

TimesFM: Foundation Model from Google

TimesFM is a pretrained foundation model for zero-shot forecasting. It requires no training on your data; first forecast in minutes.

import timesfm tfm = timesfm.TimesFm( context_len=512, horizon_len=128, input_patch_len=32, output_patch_len=128, num_layers=20, model_dims=1280, backend='gpu' ) tfm.load_from_checkpoint(repo_id="google/timesfm-1.0-200m") # Zero-shot inference forecast_input = [np.array(historical_data)] frequency_input = [0] # 0=high freq, 1=low freq point_forecast, experimental_quantile_forecast = tfm.forecast( forecast_input, freq=frequency_input ) 

Advantages: zero-shot, speed, strong results on most tasks. Limitations: does not account for known future covariates without fine-tuning, limited interpretability, requires significant context (>500 points).

Comparative Model Selection

Typical Timelines and Work Volume

Training and Validation Practice

Pipeline for training (example with Prophet):

1. Data preparation: resample to required frequency, handle missing values (interpolation / forward fill).
2. Analysis: ACF/PACF, decompose, holiday analysis.
3. Baseline: Seasonal Naive (forecast = value one year ago).
4. Prophet fit with default parameters, cross-validation.
5. Hyperparameter search: Optuna / grid search over 4–6 parameters (e.g., changepoint_prior_scale, seasonality_prior_scale, fourier_order).
6. Ensemble with competitor (ETS or NeuralProphet).
7. Productionization: Airflow DAG, MLflow tracking, API endpoint.

Metrics for comparison: SMAPE, MASE (normalized on Seasonal Naive), Winkler Score for interval forecasts.

Timelines: tuning and training Prophet/NeuralProphet for a single series with cross-validation — 1–2 weeks. Production pipeline with monitoring and auto-retraining — 4–6 weeks. Savings from accurate forecasting can cover the project cost within the first months. Get an individual offer — contact us for a consultation on your task. We will evaluate the project and offer an optimal solution.