Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting

• URL: http://arxiv.org/abs/2003.09311v1
• Date: Mon, 16 Mar 2020 10:21:37 GMT
• Title: Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting
• Authors: Anirban Chatterjee, Subhadip Paul, Uddipto Dutta, Smaranya Dey
• Abstract summary: Time series forecasting is a challenging problem due to complex and evolving nature of time series data. No one method is universally effective for all kinds of time series data. We propose a re-weighting based method to adjust the assigned weights to various forecasters in order to account for such distribution-drift.
• Score: 0.491574468325115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time Series Forecasting is at the core of many practical applications such as sales forecasting for business, rainfall forecasting for agriculture and many others. Though this problem has been extensively studied for years, it is still considered a challenging problem due to complex and evolving nature of time series data. Typical methods proposed for time series forecasting modeled linear or non-linear dependencies between data observations. However it is a generally accepted notion that no one method is universally effective for all kinds of time series data. Attempts have been made to use dynamic and weighted combination of heterogeneous and independent forecasting models and it has been found to be a promising direction to tackle this problem. This method is based on the assumption that different forecasters have different specialization and varying performance for different distribution of data and weights are dynamically assigned to multiple forecasters accordingly. However in many practical time series data-set, the distribution of data slowly evolves with time. We propose to employ a re-weighting based method to adjust the assigned weights to various forecasters in order to account for such distribution-drift. An exhaustive testing was performed against both real-world and synthesized time-series. Experimental results show the competitiveness of the method in comparison to state-of-the-art approaches for combining forecasters and handling drift.

Related papers

• StreamEnsemble: Predictive Queries over Spatiotemporal Streaming Data [0.8437187555622164]
  We propose StreamEnembles, a novel approach to predictive queries overtemporal (ST) data distributions. Our experimental evaluation reveals that this method markedly outperforms traditional ensemble methods and single model approaches in terms of accuracy and time.
  arXiv  Detail & Related papers  (2024-09-30T23:50:16Z)
• DAM: Towards A Foundation Model for Time Series Forecasting [0.8231118867997028]
  We propose a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time. It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output; and (3) the basis coefficients of a continuous function of time.
  arXiv  Detail & Related papers  (2024-07-25T08:48:07Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• Attention-Based Ensemble Pooling for Time Series Forecasting [55.2480439325792]
  We propose a method for pooling that performs a weighted average over candidate model forecasts. We test this method on two time-series forecasting problems: multi-step forecasting of the dynamics of the non-stationary Lorenz 63 equation, and one-step forecasting of the weekly incident deaths due to COVID-19.
  arXiv  Detail & Related papers  (2023-10-24T22:59:56Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization [0.0]
  It is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. We propose a framework for forecasting short high-dimensional time series data by combining low-rank temporal matrix factorization and optimal model selection on latent time series.
  arXiv  Detail & Related papers  (2021-12-15T11:39:21Z)
• Instance-wise Graph-based Framework for Multivariate Time Series Forecasting [69.38716332931986]
  We propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps. The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast.
  arXiv  Detail & Related papers  (2021-09-14T07:38:35Z)
• Global Models for Time Series Forecasting: A Simulation Study [2.580765958706854]
  We simulate time series from simple data generating processes (DGP), such as Auto Regressive (AR) and Seasonal AR, to complex DGPs, such as Chaotic Logistic Map, Self-Exciting Threshold Auto-Regressive, and Mackey-Glass equations. The lengths and the number of series in the dataset are varied in different scenarios. We perform experiments on these datasets using global forecasting models including Recurrent Neural Networks (RNN), Feed-Forward Neural Networks, Pooled Regression (PR) models, and Light Gradient Boosting Models (LGBM)
  arXiv  Detail & Related papers  (2020-12-23T04:45:52Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.