This project explores probabilistic time series forecasting using PyTorch, focusing on predicting not just single point estimates but the entire probability distribution of future values. It implements and compares various deep learning models, including DeepAR, Transformer, and N-BEATS, adapted for probabilistic outputs. The models are evaluated using metrics like quantile loss and negative log-likelihood, emphasizing the accuracy of the predicted uncertainty. The repository provides a framework for training, evaluating, and visualizing these probabilistic forecasts, enabling a more nuanced understanding of future uncertainties in time series data.
Merlion is an open-source Python machine learning library developed by Salesforce for time series forecasting, anomaly detection, and other time series intelligence tasks. It provides a unified interface for various popular forecasting models, including both classical statistical methods and deep learning approaches. Merlion simplifies the process of building and training models with automated hyperparameter tuning and model selection, and offers easy-to-use tools for evaluating model performance. It's designed to be scalable and robust, suitable for handling both univariate and multivariate time series in real-world applications.
Hacker News users discussing Merlion generally praised its comprehensive nature, covering many time series tasks in one framework. Some expressed skepticism about Salesforce's commitment to open source projects, citing previous examples of abandoned projects. Others pointed out the framework's complexity, potentially making it difficult for beginners. A few commenters compared it favorably to other time series libraries like Kats and tslearn, highlighting Merlion's broader scope and autoML capabilities, while acknowledging potential overlap. Some users requested clarification on specific features like anomaly detection evaluation and visualization capabilities. Overall, the discussion indicated interest in Merlion's potential, tempered by cautious optimism about its long-term support and usability.
Summary of Comments ( 5 )
https://news.ycombinator.com/item?id=43320194
Hacker News users discussed the practicality and limitations of probabilistic forecasting. Some commenters pointed out the difficulty of accurately estimating uncertainty, especially in real-world scenarios with limited data or changing dynamics. Others highlighted the importance of considering the cost of errors, as different outcomes might have varying consequences. The discussion also touched upon specific methods like quantile regression and conformal prediction, with some users expressing skepticism about their effectiveness in practice. Several commenters emphasized the need for clear communication of uncertainty to decision-makers, as probabilistic forecasts can be easily misinterpreted if not presented carefully. Finally, there was some discussion of the computational cost associated with probabilistic methods, particularly for large datasets or complex models.
The Hacker News post titled "Probabilistic Time Series Forecasting" (linking to a GitHub repository) generated several comments, engaging with various aspects of probabilistic forecasting.
One commenter highlighted the importance of distinguishing between probabilistic forecasting and prediction intervals, emphasizing that the former provides a full distribution over possible future values, while the latter only offers a range. They noted that many resources conflate these concepts. This commenter also questioned the practicality of evaluating probabilistic forecasts solely based on metrics like mean absolute error, suggesting that proper scoring rules, which consider the entire probability distribution, are more appropriate.
Another user questioned the value of probabilistic forecasts in certain business contexts, arguing that business decisions often require a single number rather than a probability distribution. They presented a scenario of needing to order inventory, where a single quantity must be chosen despite the inherent uncertainty in demand. This prompted a discussion about the role of quantiles in bridging the gap between probabilistic forecasts and concrete decisions. Other commenters illustrated how probabilistic forecasts can inform decision-making by allowing businesses to optimize decisions under uncertainty, for example, by considering the expected value of different order quantities. Specific examples mentioned included optimizing inventory levels to minimize expected costs or estimating the probability of exceeding a specific sales target.
The difficulty of evaluating probabilistic forecasts was another recurring theme. Commenters discussed various metrics and their limitations, with some advocating for proper scoring rules and others suggesting visual inspection of the predicted distributions. The challenge of communicating probabilistic forecasts to non-technical stakeholders was also raised.
Finally, several comments focused on specific tools and techniques for probabilistic time series forecasting, including Prophet, DeepAR, and various Bayesian methods. Some users shared their experiences with these tools and offered recommendations for specific libraries or resources.