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Results for “Time Series Forecasting”

30 findings

74match

Finding 49841 source

Integrating causal treatment/control concepts with \$return\$ sign analysis, this paper presents a novel framework for financial time series forecasting. It demonstrates potential improvements in market prediction and portfolio optimization over conventional econometric and machine learning models. Though echoing recent work, its moderately original approach offers fresh and compelling analytical insights.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

72match

Finding 49821 source

A novel causal forecasting framework trains distinct models for each return direction, applying rolling windows, hyperparameter optimization, and uplift modeling; a decision module selects optimal forecasts based on predicted directionality.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

72match

Finding 49811 source

Researchers merge causal inference and financial forecasting by dividing data into treatment (positive returns) and control (negative returns), enabling LSTMs to yield improved predictions over ARIMA and Random Walk models.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

72match

Finding 49831 source

Results reveal minor $MSE$ improvements as statistically significant via Model Confidence Set tests, with performance offset by quarterly fluctuations and modest gains, underscoring the need for research on complex categorizations.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

68match

Finding 43541 source

Comparing over a dozen forecasting approaches, the study uses datasets and strategy simulation, while limited hyperparameter tuning and a narrow focus reveal areas for improvement in financial time series evaluation.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

67match

Finding 43521 source

FinTSBridge unveils a comprehensive evaluation suite linking advanced time series forecasting models with real-world financial applications through specialized datasets encompassing global stock indices, options, and Bitcoin futures, showing practical utility.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

67match

Finding 43551 source

This paper innovatively bridges advanced time series forecasting models with financial portfolio and market challenges, employing curated datasets and introducing novel metrics such as $msIC$ and $msIR$. Its tailored, finance-specific evaluation tasks bring freshness to established methods, making it a compelling, balanced exploration of quantitative innovation in financial forecasting applications.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

67match

Finding 43531 source

The study reveals that error metrics $$MSE$$ and $$MAE$$ can mislead for non-stationary financial data, prompting new correlation metrics $$msIC$$ and $$msIR$$ which better capture temporal dependencies and model reliability.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

66match

Finding 38261 source

Researchers enhance financial forecasting by integrating topological data analysis (TDA) features, including persistent entropy, amplitude, and point count, into the N-BEATS model, improving prediction accuracy across cryptocurrencies and traditional instruments.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

65match

Finding 19711 source

The study concludes that there is significant potential for future research in UQ for financial time series prediction, particularly in combining different analysis methods and exploring Forex markets. Potential applications include improved financial forecasting and risk management strategies.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

64match

Finding 38281 source

Despite increased computational complexity and a focus on univariate forecasting, the innovative TDA-enhanced model outperformed traditional approaches, delivering insights and opening new avenues for reinforcement learning and high-frequency trading applications.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

64match

Finding 41891 source

The study demonstrates the combined power of CNN and Transformer models for financial time series forecasting, showing potential for downstream trading decisions. Future research could explore further applications and improvements of this method in financial markets.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, series, time

64match

Finding 38291 source

The paper presents an original integration of topological data analysis (TDA) with the innovative forecasting model $N\text{-BEATS}$, extracting novel features such as persistent entropy, amplitude, and point counts. Its unique approach effectively captures complex market dependencies, offering a compelling advancement in financial time series forecasting and enhanced decision-making in quantitative finance.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

64match

Finding 48351 source

This paper introduces an innovative framework integrating dynamic network models and time-varying graphs to forecast covariance matrices, addressing portfolio risk management and prediction challenges. Its novel application of higher-order ($GNAR$) interactions and Graphical Lasso enhances volatility and correlation dynamics, offering compelling, original, and impactful advancements over traditional methods in modeling.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

64match

Finding 43871 source

The research demonstrates the effectiveness of using VMD and BiLSTM networks for Bitcoin price forecasting. Potential applications include financial market analysis and investment strategies. Future research could explore different predictors and further enhance the proposed framework.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

63match

Finding 38271 source

Using a novel sliding window approach to extract multi-scale geometric features and nonlinear dependencies, the study rigorously compares TDA-augmented models with alternative techniques, validated by Friedman and Holm statistical tests.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

63match

Finding 48331 source

Integrating dynamic volatility networks, the model reduces forecasting errors and improves portfolio performance amid market turmoil, yielding smoother parameter estimates, higher Sharpe ratios, and lower turnover via risk management techniques.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

62match

Finding 66271 source

This unique empirical analysis combines established complexity measures (e.g., permutation entropy and $CH\text{-}plane$) with various forecasting models to examine cryptocurrency time-series. Its counterintuitive findingnaive models outperform advanced machine and deep learning techniqueschallenges expected norms, revealing substantial challenges in market prediction while offering an impressively novel, incremental yet impactful methodological integration.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

62match

Finding 66251 source

Surprisingly, nave forecasting models remarkably outperform sophisticated statistical, machine learning, and deep learning methods across various forecast horizons, challenging conventional wisdom and emphasizing inherent difficulties in predicting cryptocurrency price movements.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, series, time

62match

Finding 43601 source

FinZero turns time series into images and trains a 3B-parameter vision-language model with uncertainty-aware RL (UARPO) to reason, predict rises/falls, and assess confidence using Qwen2.5-VL-3B; inputs avoid normalization, window constraints.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

62match

Finding 83641 source

Authors introduce an innovative new multivariate patching strategy aggregating time series and static features with diagnostic measures based on Heavy-Tailed Self-Regularization theory, linking LLM spectral properties to enhanced forecast accuracy.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, series, time

61match

Finding 43621 source

Results: average accuracy in price 58.90% and volatility 58.33%, beating Qwen-7B and GPT-4o; contributions: diverse FVLDB plus UARPO blending IGRA, CGRA, UARA; limits: modest scale, missing metrics, calibration, binary tasks.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

61match

Finding 48321 source

Researchers present a forecasting approach merging network-based models with the HAR method, using dynamic volatility networks and higher-order interactions via data-driven CoC graphs to capture asset interdependencies and market shifts.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

61match

Finding 54961 source

The study concludes that LSTM-based portfolios offer higher returns and lower volatility compared to traditional methods. Potential applications include improved trading strategies and risk management. Future research could explore learning rate decay, systematic retraining, and weighted trading strategies to enhance performance further.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

61match

Finding 43611 source

Method uses multi-part rewards (accuracy, length, format) on two A100 GPUs; confidence-based grouping boosts usable accuracy, with UARPO fine-tuning on 10k+ FVLDB outperforming GPT-4o by 13.48% in high-confidence group evaluations.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

60match

Finding 43631 source

FinZero targets risk-aware financial forecasting by uniting chart-based multimodal LLMs with uncertainty-quantified, interpretable reasoning. Its UARPO fine-tuning blends in-group and cross-group relative advantages with uncertainty adjustment, yielding high-confidence gains over GPT-4o. A new FVLDB image-text dataset supports evaluation. Despite leveraging known GRPO/PPO ideas, integration and empirical improvements are practically compelling.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

60match

Finding 48341 source

Using rolling window estimation, winsorisation, and Graphical Lasso, the study builds sparse volatility networks and tests graph structures. It reveals sensitivity to network construction and update frequency, prompting further research.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, prediction, series, time

59match

Finding 73921 source

The research demonstrates the potential of the proposed feature engineering methodology for high-frequency financial data analysis and forecasting. Potential applications include improved financial market predictions and risk management. Future research could explore broader applications and refine the methodology for enhanced accuracy.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, series, time

59match

Finding 20241 source

AIR achieves remarkable results, reducing forecasting error rates by up to 38% in complex financial tasks like exchange rate and crude oil price prediction, thanks to text-guided internal model pathways and a language model-powered text-refinement pipeline.

LexicalMeaningQualityGraph

Matched: forecasting, prediction, series, time

59match

Finding 71341 source

Challenging the status quo, this paper reveals that simple feedforward neural networks can rival complex time series forecasting models. It introduces an original perspective on $$\text{model complexity trade-offs}$$, offering a novel, efficient baseline. Its rigorous analysis and critique of benchmarking practices make it highly compelling and impactful in diverse applications.

LexicalMeaningQualityGraph

Matched: forecast, forecasting, series, time