Improved prediction of hierarchical time series using structured regularization.
problem Making coherent forecasts for hierarchical time series.
method Structured regularization method for bottom-level time series predictions.
result Superior prediction accuracy and computational efficiency compared to previous methods.
HopCPT improves conformal prediction for time series with temporal dependencies.
problem Uncertainty quantification in time series data.
method HopCPT, a novel conformal prediction approach for time series that leverages temporal dependencies.
result HopCPT outperforms state-of-the-art methods on multiple real-world time series datasets.
JANET improves time series prediction with adaptive uncertainty regions.
problem Time series data's lack of exchangeability and multi-step prediction challenges.
method Proposes JANET, a framework for joint adaptive prediction regions with controlled error rates.
result Demonstrates superior performance in multi-step prediction tasks across diverse datasets.
New method for time series prediction with uncertainty quantification.
problem Uncertainty quantification for multi-dimensional time series predictions.
method Flow-based conformal prediction for time series.
result Significantly smaller prediction sets with target coverage.
DArtNet predicts time series data using graph structure and dynamic attributes.
problem Predicting time series data using graph structure and dynamic attributes.
method DArtNet learns static and dynamic embeddings for graph nodes and encodes history information using RNN for joint link and attribute prediction.
result Improved time series prediction accuracy on five datasets.
In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of aftereffect or memory. The technology proposes prediction with the hierarchy of time discretization intervals a…
Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.
problem Improving accuracy in time series prediction.
method Combines difference-attention LSTM and error-correction LSTM in a cascade approach.
result Improves prediction accuracy in time series.
The paper tackles time series data by applying conformal prediction with nearest neighbors.
problem Time series data violates the exchangeability assumption required for conformal prediction.
method The approach uses the nearest neighbors method with fast parameter tuning and weighted nearest neighbors (FPTO-WNN) to construct reliable prediction intervals.
result Data analysis shows the effectiveness of the proposed approach.
Timer-XL predicts multidimensional time series using a unified Transformer approach.
problem Unified time series forecasting across various tasks and contexts.
method Decoder-only Transformers with a universal TimeAttention mechanism and deft position embedding.
result State-of-the-art performance across multiple forecasting benchmarks.
timeXplain bridges AI and time series, making predictions understandable.
problem Making time series classifier predictions interpretable.
method Developed a framework that combines time series data with model-agnostic explainers.
result timeXplain improves the interpretability of time series classifiers.
A new framework for generating predictive features in noisy multivariate time series.
problem Predicting noisy multivariate time series with limited user effort.
method Develops a feature programming framework based on spin-gas dynamical Ising models.
result Validated the method on synthetic and real-world datasets.
Paper uses Time Series Transformer for bank stability prediction.
problem Predicting bank stability using complex financial data.
method Time Series Transformer model with self-attention mechanism.
result Time Series Transformer model outperforms other models in MSE and MAE.
Precise financial series predicting has long been a difficult problem because of unstableness and many noises within the series. Although Traditional time series models like ARIMA and GARCH have been researched and proved to be effective in predicting, their performances are still far from satisfying. Machine Learning,…
Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.
problem Forecasting and uncertainty quantification for Sea Level Anomalies.
method Functional data analysis, Conformal Prediction, Functional Autoregressive Processes.
result Proposed method provides accurate probabilistic predictions and uncertainty quantification for Sea Level Anomalies.
Develops a method for multivariate time series prediction intervals.
problem Uncertainty quantification in multivariate time series forecasting.
method Conformal prediction method for multivariate time series.
result Empirically demonstrates valid coverage of prediction regions.
Paper analyzes time series prediction using empirical risk minimization.
problem Optimizing 1-step-ahead prediction for time series.
method Empirical risk minimization applied to recursive algorithms for time series forecasting.
result Empirical risk minimization achieves optimal predictive performance.
This research evaluates measures of dependence for financial time-series data.
problem Accurately preparing time series data and selecting an appropriate measure of dependence is challenging.
method Review and establishment of a comprehensive analysis framework for shaping time-series data and evaluating measures of dependence.
result A method, framework, and example for selecting and evaluating a suitable measure of dependence are presented.
Study predicts synchronization state of financial time series using cross-recurrence plots.
problem Predicting the state of synchronization of financial time series.
method Cross-correlation analysis and deep learning framework for predicting synchronization state based on cross-recurrence plots.
result Satisfactory performance in predicting synchronization state for certain pairs of stocks.
Novel method for time-series prediction with tighter confidence intervals.
problem Improving prediction intervals for time-series data.
method Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI) using adaptive weights.
result KOWCPI achieves narrower confidence intervals with guaranteed coverage.
POLA adapts learning rates for online time series prediction.
problem Adapting to changing data distributions in dynamic environments.
method Adaptive learning rate regulation for recurrent neural networks.
result POLA outperforms other online prediction methods in real-world datasets.
New algorithm speeds up RNN time series prediction by filtering noise.
problem Predicting smooth trajectories from noisy time series data.
method Analyzed RNN dynamics to propose an efficient noise filtering algorithm.
result Significant speedup in predictive process without accuracy loss.
Adaptive model learns from time series data with changing distributions.
problem Predicting time series data under distribution shift.
method Formulates distribution shift as weighted empirical risk minimization. Uses a gradient-based learning method for a forgetting mechanism.
result Proposes an efficient method for adaptive time series prediction.
We present online prediction methods for time series that let us explicitly handle nonstationary artifacts (e.g. trend and seasonality) present in most real time series. Specifically, we show that applying appropriate transformations to such time series before prediction can lead to improved theoretical and empirical p…
Bi-GAN model for imputing and predicting irregular time-series data.
problem Irregularly observed, varying length time-series data with missing entries.
method Bi-GAN model using a bidirectional recurrent network in a generative adversarial setting.
result Bi-GAN model can impute and predict missing values for time-series of varying length.
Proposes a method to predict time series data using scale information.
problem Sequential data exhibits different behavior at different scales.
method Scale-based inference using k-nearest neighbors and sequential prediction. result Asymptotically, the algorithm produces the best prediction error.
Method summarizes and predicts time series data for COVID-19 cases and deaths.
problem Summarizing and predicting time series data for multiple related time series.
method Hierarchical algorithm generating shapelets for centroids, nearest neighbor search for labeling, dynamic time warping for non-uniform lengths.
result Predictive model for individual time series based on aggregated statistics.
Enhances VC startup success predictions using graph augmented time series models.
problem Challenges in predicting startup success due to limited financial data and subjective forecasts.
method Integrates inter-company relationships into time series analysis using GraphRAG.
result Significantly outperforms previous models in startup success predictions.
New method uses conformal prediction for time series forecasting, accounting for temporal correlation.
problem Uncertainty quantification in temporally correlated time series data.
method Time series decomposition with component-wise conformal prediction.
result The method provides customized prediction intervals for different temporal components.
Local surrogate model improves time series forecasts and provides interpretable explanations.
problem Improving time series forecasting accuracy while maintaining interpretability.
method A local surrogate model is used to correct the base model's predictions, making the corrections interpretable by re-fitting the base model to the error-predicted data.
result The method can discover and explain underlying patterns in the data, improving both accuracy and interpretability.
Proposes a GNN for multivariate time-series prediction with filtering.
problem Low signal-to-noise ratio in complex systems data.
method Integrates a spatial-temporal GNN with a matrix filtering module to generate filtered graphs.
result Proposed model outperforms baseline approaches in multivariate time-series prediction.
A new algorithm for time series prediction intervals.
problem Non-exchangeability in time series data.
method Adaptive re-estimation of non-conformity scores.
result Significant reduction in interval width compared to existing methods.
A new LSTM architecture improves time series forecasting efficiency.
problem Efficiency and accuracy in time series forecasting using linear models.
method Attention-free LSTM architecture for time series prediction.
result Improved prediction capacity and efficiency compared to LSTM.
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection…
Adaptive Conformal Inference improves time series forecasting uncertainty.
problem Uncertainty quantification in time series models with dependency.
method AgACI, an adaptive method based on online expert aggregation.
result AgACI provides efficient prediction intervals for day-ahead electricity price forecasting.
Financial time series prediction, especially with machine learning techniques, is an extensive field of study. In recent times, deep learning methods (especially time series analysis) have performed outstandingly for various industrial problems, with better prediction than machine learning methods. Moreover, many resea…
Proposes a method to apply conformal prediction to probabilistic time series forecasting models.
problem Obtaining accurate prediction regions for multi-step time series forecasting with probabilistic models.
method Conformalises conditional normalising flows to generate potentially disjoint prediction regions.
result Improves predictive efficiency in time series forecasting with multimodal distributions.
Foundation models improve time series prediction reliability, especially with limited data.
problem Improving time series prediction reliability with limited data.
method Comparison of Time Series Foundation Models (TSFMs) with traditional methods in conformal prediction.
result TSFMs provide more reliable conformalized prediction intervals and more stable calibration with limited data.
Study compares LSTM and Transformer models in financial time series prediction.
problem Comparing LSTM and Transformer models for financial time series prediction.
method Various LSTM-based and Transformer-based models compared on financial tasks; DLSTM and new Transformer architecture designed.
result Transformer-based models show limited advantage in absolute price sequence prediction, while LSTM-based models perform better on difference sequences.
In the era of big data, practical applications in various domains continually generate large-scale time-series data. Among them, some data show significant or potential periodicity characteristics, such as meteorological and financial data. It is critical to efficiently identify the potential periodic patterns from mas…
This work improves explanation quality for time series predictions by learning perturbations.
problem Explaining predictions on multivariate time series data with time dependencies.
method Learning both masks and associated perturbations to explain predictions.
result Learning perturbations significantly improves explanation quality on time series data.
Fine-tuning a time series model improves financial price prediction accuracy.
problem Improving accuracy in predicting financial market prices using large models.
method Continual pre-training of a time series foundation model on financial data to fine-tune its performance for price prediction.
result The fine-tuned model outperforms the baseline in various financial metrics.
Paper proposes LATC for multivariate time series prediction and missing data imputation.
problem Large-scale, incomplete, and corrupted multivariate time series data.
method Transforms multivariate time series into a tensor structure, models global and local trends, and uses autoregressive norm.
result Integration of global and local trends improves missing data imputation and rolling prediction.
Split conformal prediction works well for time series despite temporal dependence.
problem Uncertainty quantification for time series predictions with past data.
method Split conformal prediction method for time series data with predictors having memory.
result Theoretical bounds on coverage probability for split conformal prediction in time series with memory.
This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.
problem Existing prediction methods often ignore the distinction between long-term trends and short-term fluctuations.
method The paper introduces a MTS forecasting framework that uses both original time series and its first difference to capture long-term trends and short-term fluctuations.
result The proposed method improves forecasting performance by using more supervision information.
A new method clusters time series based on model prediction accuracy.
problem Clustering time series data effectively.
method Iterative model fitting and assignment based on predictive accuracy.
result The method outperforms other techniques in clustering and predictive accuracy.
Model predicts competition between similar products in sales.
problem Predicting cannibalization between similar products in sales.
method Developed a neural network model that computes a 'competitiveness' function based on product features.
result The model outperforms traditional methods in predicting market share.
Paper introduces Native Guide for generating time series counterfactual explanations.
problem Lack of explainability for time series data in AI systems.
method Model-agnostic, instance-based counterfactual generation for time series classification.
result Native Guide produces better counterfactual explanations than benchmarks.
LINTEL improves INTEL's time series prediction by optimizing computation and accuracy.
problem Online prediction of time series with regime switching and outliers.
method Gaussian process-based approach with exact filtering distribution and constant-time updates.
result LINTEL is over five times faster with better quality predictions.