CounterfacTS: Strengthening Time-Series Forecasting Models

Time-series Forecasting is a common task in many fields, including finance, weather prediction, and supply chain management. The goal is to predict future values based on past data. However, one big challenge is that the patterns in the data can change over time. This situation, called concept drift, can make predictive models less reliable when facing new or different scenarios.

To address this issue, we introduce a tool named CounterfacTS. This tool helps evaluate how well forecasting models perform when faced with different situations by creating Counterfactuals. Counterfactuals are alternative scenarios that allow users to explore "what-if" scenarios-what would happen if things were different? With CounterfacTS, users can visualize, compare, and understand time-series data and their forecasts effectively.

#Concept of Counterfactuals in Forecasting

Counterfactual reasoning is a way of thinking about alternative outcomes based on changes in underlying factors. For example, if a certain event occurred differently, how would the outcome change? This type of reasoning is not limited to humans; it is increasingly relevant in artificial intelligence as well. Counterfactuals can help us anticipate how different scenarios might impact outcomes that were not covered in the original data.

In the context of time-series forecasting, counterfactuals can be beneficial, especially when test data is situated at the edges or outside the area covered by the training data. Models tend to perform poorly in these situations since they were not trained on examples from these regions. If the training data does not change but the future data does (due to concept drift), predictive performance can decline sharply.

With CounterfacTS, we aim to create counterfactuals that help fill gaps in the training data, improving Model Performance in less covered areas.

#Importance of Visualizing Data

Understanding how different characteristics of Time Series impact forecasting performance is essential. This process begins with visualizing the data and assessing how these elements relate to each other. CounterfacTS provides tools that allow users to visualize the distribution of time-series data in a two-dimensional feature space. This visualization enables users to see patterns, identify characteristics that matter for forecasting, and evaluate model performance.

By placing different time series in this feature space, we can observe how their characteristics affect predictions. In doing so, we can pinpoint which features most influence forecasting accuracy and how to adjust the original time series to create counterfactuals with desired outcomes.

#User-Friendly Interface

The CounterfacTS tool features an easy-to-use graphical interface that allows users to interact with time-series data seamlessly. Through this interface, users can:

• Visualize data distributions in the feature space.
• Select individual time series to analyze.
• Apply various transformations to modify time series.
• Observe how these changes affect forecasting predictions.

This interface also provides performance metrics, allowing users to easily compare predictions with actual outcomes and assess how different transformations have improved or worsened results.

#Exploring Time-Series Features

To enhance time-series forecasting, it is crucial to understand and modify the features that characterize these series. Within CounterfacTS, we focus on four main features that can be modified:

1. Trend Determination: This feature assesses how much the underlying trend contributes to the series. It can be modified to strengthen or weaken the apparent trend.

2. Trend Slope: This feature indicates the rate of change of the trend. Users can adjust the slope to create a steeper or gentler trend, affecting forecasting results.

3. Trend Linearity: This feature describes how closely the trend follows a straight line. Modifying this allows users to create trends that are either more straight or more variable.

4. Season Determination: This feature looks at how much seasonality impacts the time series. By adjusting it, users can emphasize or reduce seasonal patterns.

These features can be changed individually using sliders in the CounterfacTS interface. This interactivity allows users to see the immediate impact of each adjustment on both the visual representation of the time series and the forecast results.

#Transforming Time-Series Data

CounterfacTS allows users to transform time series effectively. Users can apply general modifications, like shifting the whole series up or down, or they can focus on specific features. This flexibility is critical for exploring how different scenarios might play out in future predictions.

For example, if a user wants to see how a sharp increase in values might affect predictions, they could modify the trend component to reflect that change. Similarly, users can introduce noise to simulate real-world uncertainties or change the strength of seasonal patterns.

#Study Cases: Evaluating Model Performance

To illustrate the benefits of using CounterfacTS, we can explore two study cases. The first involves investigating how forecasting performance varies with the properties and positions of time series in the feature space. The second focuses on applying counterfactual data to improve model performance in underrepresented areas.

#Case 1: Performance Variation

In this case, we examine how the location of time series influences forecasting accuracy. By visualizing data in the feature space, we can see areas with sparse training coverage. Metrics such as Mean Absolute Scaled Error (MASE) can help quantify the performance of models in these regions.

By using CounterfacTS, we find that time series in poorly covered areas often see a significant drop in prediction accuracy. This finding aligns with our understanding that models struggle when they encounter data that looks different from what they were trained on. By identifying the features that matter most, we can create targeted transformations to improve model robustness in these weak areas.

#Case 2: Improving Robustness with Counterfactuals

In the second scenario, we look at how counterfactuals can enhance model reliability. After identifying specific features that limit performance, we generate new time series that reflect characteristics observed in better-performing examples. By training models with these counterfactuals, we can fill gaps within the training data.

The results reveal that models trained with counterfactual data perform significantly better in undersampled regions. Even when considering outliers, using counterfactuals improves the median forecasting performance, demonstrating the value of exploring alternative scenarios.

#Conclusion

CounterfacTS is a powerful tool for improving time-series forecasting models. By creating counterfactuals and visualizing how different features affect performance, users can gain valuable insights into their data. This approach helps address the challenges posed by concept drift and ensures that models remain reliable over time.

Through its user-friendly interface, CounterfacTS enables users to explore and manipulate time-series data easily. By adjusting various features, visualizing their impact, and generating counterfactual scenarios, users can elevate their forecasting capabilities.

As time-series data continues to evolve, tools like CounterfacTS will be essential for maintaining model reliability and making informed decisions. By harnessing the potential of counterfactual reasoning, we can better anticipate future scenarios and adapt our models accordingly, paving the way for improved predictive performance across various fields.