5 Python Libraries for Advanced Time Series Forecasting
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Introduction
Predicting the future has always been the holy grail of analytics. Whether it is optimizing supply chain logistics, managing energy grid loads, or anticipating financial market volatility, time series forecasting is often the engine driving critical decision-making. However, while the concept is simple — using historical data to predict future values — the execution is notoriously difficult. Real-world data rarely adheres to the clean, linear trends found in introductory textbooks.
Fortunately, Python’s ecosystem has evolved to meet this demand. The landscape has shifted from purely statistical packages to a rich array of libraries that integrate deep learning, machine learning pipelines, and classical econometrics. But with so many options, choosing the right framework can be overwhelming.
This article cuts through the noise to focus on 5 powerhouse Python libraries designed specifically for advanced time series forecasting. We move beyond the basics to explore tools capable of handling high-dimensional data, complex seasonality, and exogenous variables. For each library, we provide a high-level overview of its standout features and a concise “Hello World” code snippet to familiarize yourself immediately.
1. Statsmodels
statsmodels provides best-in-class models for non-stationary and multivariate time series forecasting, primarily based on methods from statistics and econometrics. It also offers explicit control over seasonality, exogenous variables, and trend components.
This example shows how to import and use the library’s SARIMAX model (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors):
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from statsmodels.tsa.statespace.sarimax import SARIMAX
model = SARIMAX(y, exog=X, order=(1,1,1), seasonal_order=(1,1,1,12)) res = model.fit() forecast = res.forecast(steps=12, exog=X_future) |
2. Sktime
Fan of scikit-learn? Good news! sktime mimics the popular machine learning library’s style framework-wise, and it is suited for advanced forecasting tasks, enabling panel and multivariate forecasting through machine-learning model reduction and pipeline composition.
For instance, the make_reduction() function takes a machine-learning model as a base component and applies recursion to perform predictions multiple steps ahead. Note that fh is the “forecasting horizon,” allowing prediction of n steps, and X_future is meant to contain future values for exogenous attributes, should the model utilize them.
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from sktime.forecasting.compose import make_reduction from sklearn.ensemble import RandomForestRegressor
forecaster = make_reduction(RandomForestRegressor(), strategy=“recursive”) forecaster.fit(y_train, X_train) y_pred = forecaster.predict(fh=[1,2,3], X=X_future) |
3. Darts
The Darts library stands out for its simplicity compared to other frameworks. Its high-level API combines classical and deep learning models to address probabilistic and multivariate forecasting problems. It also captures past and future covariates effectively.
This example shows how to use Darts’ implementation of the N-BEATS model (Neural Basis Expansion Analysis for Interpretable Time Series Forecasting), an accurate choice to handle complex temporal patterns.
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from darts.models import NBEATSModel
model = NBEATSModel(input_chunk_length=24, output_chunk_length=12, n_epochs=10) model.fit(series, verbose=True) forecast = model.predict(n=12) |
5 Python Libraries for Advanced Time Series Forecasting: A Simple Comparison
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4. PyTorch Forecasting
For high-dimensional and large-scale forecasting problems with massive data, PyTorch Forecasting is a solid choice that incorporates state-of-the-art forecasting models like Temporal Fusion Transformers (TFT), as well as tools for model interpretability.
The following code snippet illustrates, in a simplified fashion, the use of a TFT model. Although not explicitly shown, models in this library are typically instantiated from a TimeSeriesDataSet (in the example, dataset would play that role).
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from pytorch_forecasting import TemporalFusionTransformer
tft = TemporalFusionTransformer.from_dataset(dataset) tft.fit(train_dataloader) pred = tft.predict(val_dataloader) |
5. GluonTS
Lastly, GluonTS is a deep learning–based library that specializes in probabilistic forecasting, making it ideal for handling uncertainty in large time series datasets, including those with non-stationary characteristics.
We wrap up with an example that shows how to import GluonTS modules and classes — training a Deep Autoregressive model (DeepAR) for probabilistic time series forecasting that predicts a distribution of possible future values rather than a single point forecast:
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from gluonts.model.deepar import DeepAREstimator from gluonts.mx.trainer import Trainer
estimator = DeepAREstimator(freq=“D”, prediction_length=14, trainer=Trainer(epochs=5)) predictor = estimator.train(train_data) |
Wrapping Up
Choosing the right tool from this arsenal depends on your specific trade-offs between interpretability, training speed, and the scale of your data. While classical libraries like Statsmodels offer statistical rigor, modern frameworks like Darts and GluonTS are pushing the boundaries of what deep learning can achieve with temporal data. There is rarely a “one-size-fits-all” solution in advanced forecasting, so we encourage you to use these snippets as a launchpad for benchmarking multiple approaches against one another. Experiment with different architectures and exogenous variables to see which library best captures the nuances of your signals.
The tools are available; now it’s time to turn that historical noise into actionable future insights.
