Improving Time Series Forecasting by Addressing Imbalanced Data through Synthetic Sample Generation

Core Concepts

Framing time series forecasting problems involving multiple time series as an imbalanced learning task, and using resampling strategies to generate synthetic samples for underrepresented time series to improve forecasting accuracy.

Abstract

The paper proposes a novel method called Time Series Entity Resampler (TSER) to address the imbalanced data problem in time series forecasting tasks involving multiple time series. Key highlights: Time series forecasting problems with multiple time series can be framed as an imbalanced learning problem, where the observations for a particular time series of interest represent a small fraction of the overall dataset. TSER leverages oversampling techniques like SMOTE, ADASYN, and Borderline SMOTE to generate synthetic samples for the underrepresented time series of interest, with the goal of improving the local-global trade-off in forecasting. Experiments on 7 datasets with a total of 5502 time series show that TSER variants outperform both global and local forecasting models for the target time series, achieving a better balance between the two approaches. The paper also analyzes the sensitivity of TSER to different ways of integrating the synthetic samples and the optimal sampling ratio between the time series of interest and the other time series. While TSER improves forecasting accuracy for the target time series, it can decrease performance on other time series in the collection, as the model becomes more tailored to the specific time series.

Stats

The observations concerning a single time series of interest represent only 0.1% of the total data points in a collection with 1000 equally-sized time series. TSER(SMOTE) and TSER(ADASYN) show the best average rank across all time series, outperforming both the global and local reference models. On average, the MASE of the Local model increases by 0.12 when applied to other time series besides the target one, while the oversampling versions of TSER show a smaller decrease in performance. The best sampling ratio between the time series of interest and the other time series is around 2:1, where one-third of the samples represent the time series of interest.

Quotes

"We hypothesize that global models can miss the nuances of a particular time series of interest due to an imbalance issue. This imbalance arises from the condition that the observations representing a time series of interest represent a small fraction of the whole dataset." "Resampling strategies such as SMOTE [7] are an effective approach to tackling imbalanced domain learning problems. These work by under- or oversampling the dataset to alter the data distribution."

Key Insights Distilled From

Time Series Data Augmentation as an Imbalanced Learning Problem

by Vito... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18537.pdf

Deeper Inquiries

How can the proposed TSER method be extended to handle collections of multivariate time series or time series with exogenous variables

The TSER method can be extended to handle collections of multivariate time series or time series with exogenous variables by adapting the resampling process to incorporate the additional dimensions present in the data. When dealing with multivariate time series, each series can be treated as a separate entity, and the resampling algorithm can be modified to create synthetic samples that maintain the relationships between the variables within each series. This can involve generating new instances that preserve the correlations and dependencies among the variables in the multivariate time series. For time series with exogenous variables, the TSER method can be adjusted to consider the interactions between the endogenous time series data and the exogenous variables. The resampling process can be tailored to create synthetic samples that reflect the influence of the exogenous variables on the time series data. By incorporating the exogenous variables into the resampling algorithm, the TSER approach can effectively balance the dataset while capturing the impact of external factors on the forecasting model. In both cases, the key is to design the resampling strategy in a way that maintains the integrity of the multivariate or exogenous relationships within the data while addressing the imbalance in the dataset. By customizing the resampling process to account for the specific characteristics of multivariate time series or time series with exogenous variables, the TSER method can be extended to handle a broader range of complex data structures.

What other techniques besides resampling could be explored to address the local-global trade-off in time series forecasting with multiple time series

Besides resampling, several other techniques can be explored to address the local-global trade-off in time series forecasting with multiple time series. Some of these techniques include: Clustering Methods: Clustering similar time series together and training separate models for each cluster can help capture both global patterns shared across clusters and local patterns unique to individual time series. By grouping similar series, clustering methods can enhance the forecasting accuracy by leveraging similarities within clusters. Ensemble Learning: Ensemble methods, such as combining forecasts from multiple models, can provide a balanced approach between global and local modeling. Techniques like model averaging, stacking, or boosting can integrate predictions from diverse models to improve overall forecasting performance. Transfer Learning: Leveraging knowledge from related time series or domains to enhance the forecasting of a specific time series can be beneficial. Transfer learning techniques adapt models trained on one dataset to another, allowing the transfer of learned patterns and relationships to improve forecasting accuracy. Feature Engineering: Creating informative features that capture both global trends and local patterns in the time series data can help in achieving a better trade-off. Feature engineering techniques like trend decomposition, seasonality extraction, or lagged variables can enhance the model's ability to capture complex patterns. By exploring these alternative techniques in conjunction with resampling methods, a more comprehensive approach can be developed to address the local-global trade-off in time series forecasting with multiple time series.

What are the potential applications of the TSER approach beyond time series forecasting, such as in other domains with imbalanced data

The TSER approach has potential applications beyond time series forecasting, especially in domains with imbalanced data where the local-global trade-off is a critical consideration. Some potential applications of the TSER approach include: Anomaly Detection: In anomaly detection tasks where the majority of instances are normal and only a small fraction are anomalies, TSER can be used to generate synthetic samples of anomalies to balance the dataset. By oversampling the rare class, TSER can improve the detection of anomalies in imbalanced datasets. Fraud Detection: Similar to anomaly detection, fraud detection often deals with imbalanced data where fraudulent transactions are rare compared to legitimate ones. TSER can help in creating synthetic instances of fraudulent activities to enhance the detection capabilities of fraud detection models. Medical Diagnosis: In healthcare applications, where certain medical conditions are rare compared to normal cases, TSER can be applied to augment the dataset with synthetic samples of rare conditions. This can improve the accuracy of diagnostic models by addressing the imbalance in the data. Natural Language Processing: In text classification tasks where certain classes are underrepresented, TSER can be utilized to balance the dataset by generating synthetic instances of minority classes. This can enhance the performance of text classification models on imbalanced text data. By adapting the TSER approach to these diverse domains, it can effectively address imbalanced data challenges and improve the performance of machine learning models in various applications beyond time series forecasting.