Forecasting Foreign Exchange Rates With Parameter-Free Regression Networks Tuned By Bayesian Optimization

• URL: http://arxiv.org/abs/2204.12914v1
• Date: Tue, 26 Apr 2022 13:32:50 GMT
• Title: Forecasting Foreign Exchange Rates With Parameter-Free Regression Networks Tuned By Bayesian Optimization
• Authors: Linwei Li, Paul-Amaury Matt, Christian Heumann
• Abstract summary: The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates. To address this problem, we introduce a parameter-free regression network termed RegPred Net.
• Score: 4.642930060629522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates. To address this problem, we introduce a parameter-free regression network termed RegPred Net. The exchange rate to forecast is treated as a stochastic process. It is assumed to follow a generalization of Brownian motion and the mean-reverting process referred to as the generalized Ornstein-Uhlenbeck (OU) process, with time-dependent coefficients. Using past observed values of the input time series, these coefficients can be regressed online by the cells of the first half of the network (Reg). The regressed coefficients depend only on - but are very sensitive to - a small number of hyperparameters required to be set by a global optimization procedure for which, Bayesian optimization is an adequate heuristic. Thanks to its multi-layered architecture, the second half of the regression network (Pred) can project time-dependent values for the OU process coefficients and generate realistic trajectories of the time series. Predictions can be easily derived in the form of expected values estimated by averaging values obtained by Monte Carlo simulation. The forecasting accuracy on a 100 days horizon is evaluated for several of the most important FX rates such as EUR/USD, EUR/CNY, and EUR/GBP. Our experimental results show that the RegPred Net significantly outperforms ARMA, ARIMA, LSTMs, and Autoencoder-LSTM models in this task.

Related papers

• Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update [60.414548453838506]
  We study the generalized linear bandit (GLB) problem, a contextual multi-armed bandit framework that extends the classical linear model by incorporating a non-linear link function.<n>GLBs are widely applicable to real-world scenarios, but their non-linear nature introduces significant challenges in achieving both computational and statistical efficiency.<n>We propose a jointly efficient algorithm that attains a nearly optimal regret bound with $mathcalO(1)$ time and space complexities per round.
  arXiv  Detail & Related papers  (2025-07-16T02:24:21Z)
• Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation [2.826553192869411]
  Reconciliation is crucial for organizations requiring coherent predictions across multiple aggregation levels.<n>Current methods like minimum trace (MinT) are mostly limited to tree structures and are computationally expensive.<n>We introduce FlowRec, which reformulates hierarchical forecast reconciliation as a network flow optimization.
  arXiv  Detail & Related papers  (2025-05-06T20:16:28Z)
• Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach [51.76826149868971]
  Policy evaluation via Monte Carlo simulation is at the core of many MC Reinforcement Learning (RL) algorithms. We propose as a quality index a surrogate of the mean squared error of a return estimator that uses trajectories of different lengths. We present an adaptive algorithm called Robust and Iterative Data collection strategy Optimization (RIDO)
  arXiv  Detail & Related papers  (2024-10-17T11:47:56Z)
• Application of Deep Learning for Factor Timing in Asset Management [21.212548040046133]
  More flexible models have better performance in explaining the variance in factor premium of the unseen period. For flexible models like neural networks, the optimal weights based on their prediction tend to be unstable. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs.
  arXiv  Detail & Related papers  (2024-04-27T21:57:17Z)
• DASA: Delay-Adaptive Multi-Agent Stochastic Approximation [64.32538247395627]
  We consider a setting in which $N$ agents aim to speedup a common Approximation problem by acting in parallel and communicating with a central server. To mitigate the effect of delays and stragglers, we propose textttDASA, a Delay-Adaptive algorithm for multi-agent Approximation.
  arXiv  Detail & Related papers  (2024-03-25T22:49:56Z)
• A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series [0.0]
  Indian Summer Monsoon Rainfall (ISMR) is a very complex time series. Conventional one-time decomposition technique suffers from a leak of information from the future. Moving Front (MF) method is proposed to prevent data leakage.
  arXiv  Detail & Related papers  (2023-03-11T12:07:26Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• Deep Equilibrium Optical Flow Estimation [80.80992684796566]
  Recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms. These RNNs impose large computation and memory overheads, and are not directly trained to model such stable estimation. We propose deep equilibrium (DEQ) flow estimators, an approach that directly solves for the flow as the infinite-level fixed point of an implicit layer.
  arXiv  Detail & Related papers  (2022-04-18T17:53:44Z)
• Convolutional-Recurrent Neural Network Proxy for Robust Optimization and Closed-Loop Reservoir Management [0.0]
  A convolutional-recurrent neural network (CNN-RNN) proxy model is developed to predict well-by-well oil and water rates. This capability enables the estimation of the objective function and nonlinear constraint values required for robust optimization.
  arXiv  Detail & Related papers  (2022-03-14T22:11:17Z)
• Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting [4.716034416800441]
  This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR) EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), is suitable for nonstationary and heteroscedastic time series data, and can be applied on top of any forecasting model. The results demonstrate that EnCQR outperforms models based only on quantile regression or conformal prediction, and it provides sharper, more informative, and valid PIs.
  arXiv  Detail & Related papers  (2022-02-17T16:54:20Z)
• Bilinear Input Normalization for Neural Networks in Financial Forecasting [101.89872650510074]
  We propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme takes into account the bimodal characteristic of financial time-series. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data, show significant improvements over other normalization techniques.
  arXiv  Detail & Related papers  (2021-09-01T07:52:03Z)
• Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models [7.217783736464403]
  Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space. We propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and the weights of a neural network surrogate model.
  arXiv  Detail & Related papers  (2021-08-28T14:31:45Z)
• Parameter Estimation for the SEIR Model Using Recurrent Nets [28.065980818744208]
  We find the optimal $Theta_textSEIR$ based on the differentiable objective. We observe that the proposed strategy leads to significantly better parameter estimations with a smaller number of simulations.
  arXiv  Detail & Related papers  (2021-05-30T12:51:45Z)
• SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression [68.66245730450915]
  We develop an improved method for debiasing predictions and estimating frequentist uncertainty for practical datasets. Our main contribution is SLOE, an estimator of the signal strength with convergence guarantees that reduces the computation time of estimation and inference by orders of magnitude.
  arXiv  Detail & Related papers  (2021-03-23T17:48:56Z)

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.