insight - Machine Learning - # Zero-Shot Time-Series Forecasting

A Versatile Decoder-Only Foundation Model for Accurate Zero-Shot Time-Series Forecasting

Q: How can the foundation model be further improved to handle missing values, outliers, and other data quality issues in the input time series?

To enhance the foundation model's ability to handle missing values, outliers, and data quality issues in input time series, several strategies can be implemented: Missing Values Handling: Imputation Techniques: Implement imputation methods such as mean imputation, forward-fill, or backward-fill to fill in missing values in the time series data. Model-Based Imputation: Utilize predictive models like linear regression or K-nearest neighbors to impute missing values based on the available data. Outlier Detection and Treatment: Outlier Detection: Incorporate outlier detection algorithms like Z-score, IQR, or isolation forests to identify and flag outliers in the time series. Outlier Handling: Develop mechanisms to either remove outliers, replace them with interpolated values, or adjust them based on neighboring data points. Data Quality Checks: Data Validation: Implement checks to ensure data consistency, accuracy, and integrity before feeding it into the model. Data Cleaning: Apply data cleaning techniques to address inconsistencies, duplicates, or errors in the time series dataset. Robust Modeling Techniques: Robust Algorithms: Utilize robust forecasting algorithms that are less sensitive to outliers and missing values, such as robust regression or ensemble methods. Ensemble Approaches: Combine multiple models to mitigate the impact of outliers and missing values on individual models. Feature Engineering: Feature Creation: Engineer features that are robust to missing values and outliers, such as lagged variables, moving averages, or seasonality indicators. Feature Selection: Use feature selection techniques to focus on the most informative features while disregarding noisy or unreliable data. By incorporating these strategies, the foundation model can improve its resilience to missing values, outliers, and data quality issues, leading to more accurate and reliable forecasts.

Core Concepts

A single pre-trained foundation model, TimesFM, can achieve close to state-of-the-art zero-shot forecasting performance on a diverse set of previously unseen time-series datasets across different domains, forecasting horizons and temporal granularities.

Abstract

The paper presents TimesFM, a decoder-only foundation model for time-series forecasting that can achieve accurate zero-shot performance on a variety of previously unseen datasets. The key elements of the model are:

A large-scale time-series pretraining corpus built using real-world data (web search queries, Wikipedia page visits) and synthetic data, providing the necessary volume and diversity of data.
A decoder-style attention architecture with input patching, which can efficiently pre-train on the time-series corpus and adapt to variable context and horizon lengths at inference.
Careful design choices like using longer output patches compared to input patches, and a specific random masking strategy during training to enable the model to handle all possible context lengths.

Experiments show that TimesFM can match or outperform specialized supervised forecasting models on benchmark datasets like Monash, Darts and Informer, without any additional training on these datasets. This demonstrates the strong generalization capability of the pre-trained foundation model. Ablation studies validate the importance of design choices like synthetic data inclusion and output patch length. Overall, TimesFM represents a practical and versatile zero-shot forecasting solution that can significantly reduce the burden on downstream users.

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Stats

The pretraining dataset consists of over 100 billion time-series data points from sources like Google Trends, Wikipedia page visits, and synthetic data.
The dataset covers a diverse range of domains, temporal granularities, and time series lengths.

Quotes

"Can large pretrained models trained on massive amounts of time-series data learn temporal patterns that can be useful for time-series forecasting on previously unseen datasets?"
"Unlike in NLP, there is no well defined vocabulary or grammar for time-series. Additionally, such a model would need to support forecasting with varying history lengths (context), prediction lengths (horizon) and time granularities."

Key Insights Distilled From

A decoder-only foundation model for time-series forecasting

by Abhimanyu Da... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2310.10688.pdf

A decoder-only foundation model for time-series forecasting

Deeper Inquiries

How can the foundation model be further improved to handle missing values, outliers, and other data quality issues in the input time series?

To enhance the foundation model's ability to handle missing values, outliers, and data quality issues in input time series, several strategies can be implemented:

Missing Values Handling:

Imputation Techniques: Implement imputation methods such as mean imputation, forward-fill, or backward-fill to fill in missing values in the time series data.
Model-Based Imputation: Utilize predictive models like linear regression or K-nearest neighbors to impute missing values based on the available data.

Outlier Detection and Treatment:

Outlier Detection: Incorporate outlier detection algorithms like Z-score, IQR, or isolation forests to identify and flag outliers in the time series.
Outlier Handling: Develop mechanisms to either remove outliers, replace them with interpolated values, or adjust them based on neighboring data points.

Data Quality Checks:

Data Validation: Implement checks to ensure data consistency, accuracy, and integrity before feeding it into the model.
Data Cleaning: Apply data cleaning techniques to address inconsistencies, duplicates, or errors in the time series dataset.

Robust Modeling Techniques:

Robust Algorithms: Utilize robust forecasting algorithms that are less sensitive to outliers and missing values, such as robust regression or ensemble methods.
Ensemble Approaches: Combine multiple models to mitigate the impact of outliers and missing values on individual models.

Feature Engineering:

Feature Creation: Engineer features that are robust to missing values and outliers, such as lagged variables, moving averages, or seasonality indicators.
Feature Selection: Use feature selection techniques to focus on the most informative features while disregarding noisy or unreliable data.

By incorporating these strategies, the foundation model can improve its resilience to missing values, outliers, and data quality issues, leading to more accurate and reliable forecasts.

How can the foundation model be extended to support probabilistic forecasting and quantile estimation, in addition to point forecasts?

To extend the foundation model to support probabilistic forecasting and quantile estimation alongside point forecasts, the following approaches can be implemented:

Probabilistic Forecasting:

Output Distribution: Modify the model architecture to predict the parameters of a probability distribution (e.g., Gaussian, Poisson) instead of point estimates.
Uncertainty Estimation: Incorporate techniques like Monte Carlo dropout, Bayesian neural networks, or ensemble methods to capture prediction uncertainty.

Quantile Estimation:

Quantile Regression: Train the model to directly predict quantiles of the target distribution, enabling the estimation of different confidence levels.
Loss Function: Define a quantile loss function that penalizes deviations from the predicted quantiles, encouraging the model to learn the entire distribution.

Ensemble Methods:

Ensemble of Models: Train multiple instances of the foundation model with different initializations or architectures to capture diverse perspectives on the forecast distribution.
Weighted Averaging: Combine the predictions from multiple models using weighted averaging to generate more accurate probabilistic forecasts.

Evaluation Metrics:

Quantile Loss: Assess the model's performance using quantile loss metrics to evaluate its ability to predict different quantiles effectively.
Calibration Plots: Generate calibration plots to assess the model's calibration of predicted quantiles with observed values.

Model Interpretation:

Prediction Intervals: Provide prediction intervals around point forecasts to convey the uncertainty associated with each prediction.
Visualization: Visualize the forecast distribution using probability density plots or cumulative distribution functions to communicate the range of possible outcomes.

By incorporating these techniques, the foundation model can evolve to provide probabilistic forecasts and quantile estimates, offering valuable insights into the uncertainty of predictions and enabling more informed decision-making.