TiDE uses MLP for fast, simple long-term time-series forecasting.
problem Long-term time-series forecasting challenges.
method Time-series Dense Encoder (TiDE) based on MLP.
result TiDE matches or outperforms Transformer models while being 5-10x faster.
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.
Introduces Spectral Attention for better long-range time series forecasting.
problem Challenges in capturing long-range dependencies in time series forecasting.
method Spectral Attention mechanism that preserves temporal correlations and long-range dependencies.
result Achieves state-of-the-art results on 11 real-world time series datasets.
MPPN network improves long-term time series forecasting accuracy.
problem Inaccurate long-term time series forecasting due to noise and lack of interpretability.
method MPPN network constructs context-aware multi-resolution semantic units and employs multi-periodic pattern mining and channel adaptive module.
result MPPN significantly outperforms state-of-the-art methods on nine real-world benchmarks.
fSDE-Net generates time series with long-term memory using neural networks.
problem Generating time series with long-term memory from irregularly sampled data.
method fSDE-Net: neural fractional Stochastic Differential Equation Network using fractional Brownian motion.
result fSDE-Net can replicate distributional properties of real time-series data.
TimeBridge addresses non-stationarity in long-term time series forecasting.
problem Non-stationarity in multivariate time series leads to spurious regressions and obscures long-term relationships.
method TimeBridge segments series into patches, applying Integrated Attention for short-term non-stationarity and Cointegrated Attention for long-term cointegration.
result TimeBridge achieves state-of-the-art performance in both short-term and long-term forecasting.
Combines CNN and Transformer for financial time series forecasting.
problem Forecasting financial time series, especially stock prices, is challenging due to short-term and long-term dependencies.
method Uses CNN for short-term dependencies and Transformer for long-term dependencies.
result Demonstrated superior performance in forecasting stock price changes compared to traditional methods.
W-Transformers use wavelets to improve time series forecasting.
problem Forecasting non-stationary time series with long-range dependencies.
method Wavelet-based transformer architecture.
result W-Transformers outperform baseline models on various time series datasets.
Preformer improves Transformer for long-term time series forecasting.
problem Transformer's quadratic complexity and lack of context-awareness for long-term forecasting.
method Introduces Multi-Scale Segment-Correlation mechanism for efficient time series segmentation and context-aware attention.
result Preformer outperforms other Transformer-based methods in long-term time series forecasting.
We study the long-term memory in diverse stock market indices and foreign exchange rates using the Detrended Fluctuation Analysis(DFA). For all daily and high-frequency market data studied, no significant long-term memory property is detected in the return series, while a strong long-term memory property is found in th…
Bayesian method models financial time series with non-stationarity and dependency.
problem Discrimination between non-stationarity and long-range dependency in financial time series.
method Adaptive spectral technique using non-parametric Bayesian inference with Reversible Jump Markov Chain Monte Carlo.
result Bayesian method effectively models both long-range dependency and non-stationarity in financial time series.
PureTS uses simple linear models to improve long-term time series forecasting.
problem Improving long-term time series forecasting with complex models.
method Developed PureTS with three pure linear layers.
result PureTS achieves state-of-the-art performance in long sequence prediction tasks.
KEDformer improves long-term time series forecasting with seasonal-trend decomposition.
problem Accurate long-term predictions in energy, finance, and meteorology.
method Knowledge extraction-driven framework integrating seasonal-trend decomposition.
result KEDformer enhances model's ability to capture short-term and long-term patterns.
Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, ui, can be detected and quantified by studying the correlations in the magnitude series ∣ui∣, i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact …
Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values i…
LSTM-FCN improves time series classification with minimal model size increase.
problem Classifying time series sequences with high accuracy.
method Augmented fully convolutional networks with LSTM sub-modules and attention mechanism.
result LSTM-FCN achieves state-of-the-art performance.
Transformers improve time series modeling by capturing long-range dependencies.
problem Capturing long-range dependencies in time series data.
method Summarized and reviewed adaptations of Transformers for time series analysis.
result Transformers enhance time series forecasting, anomaly detection, and classification.
TSLANet improves time series models by capturing long-term and short-term interactions.
problem Noise sensitivity, computational efficiency, and overfitting in Transformer-based models for time series data.
method Adaptive Spectral Block and Interactive Convolution Block for robust feature representation and noise mitigation.
result TSLANet outperforms state-of-the-art models in various time series tasks.
AR-Net models time-series with interpretable coefficients and scalability.
problem Modeling time-series with long-range dependencies and interpretability.
method Feed-forward neural network approach to AR-process dynamics.
result AR-Net learns identical AR-coefficients as Classic-AR and scales to long-range dependencies.
Chinese stock market shows time series momentum and contrarian effects over different periods.
problem Analyzing momentum and contrarian effects in Chinese stock market performance.
method Examined time series momentum and contrarian strategies applied to major indices in China.
result Time series momentum effect in short run, contrarian effect in long run, performance dependent on look-back and holding periods.
New SGMCMC method controls bias in SSMs for long time series.
problem Inference in SSMs is computationally prohibitive for long time series.
method Proposed new stochastic gradient estimators to control bias in SSMs.
result Developed novel SGMCMC samplers for various SSM types.
Improved NODEs for long-term time series forecasting.
problem Dealing with complex, multi-frequency data.
method Progressive learning paradigm with curriculum learning.
result Performance improved by over 64%.
DSTP-RNN improves long-term multivariate time series prediction using attention-based RNN.
problem Long-term prediction of multivariate time series with spatial correlations and spatio-temporal relationships.
method Inspired by human attention mechanism, DSTP-RNN uses a dual-stage two-phase structure and multiple attentions to enhance spatial correlations and long-term dependence.
result DSTP-RNN outperforms nine baseline methods on four datasets in energy, finance, environment, and medicine.
New RNN model handles long-term dependencies in irregularly-sampled time series.
problem Handling long-term dependencies in irregularly-sampled time series data.
method Designing ODE-LSTMs that separate memory from continuous-time state.
result ODE-LSTMs outperform other RNN-based models on non-uniformly sampled data with long-term dependencies.
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.
Long short-term memory network outperforms seasonal model in JSE Top 40 forecasting.
problem Comparing neural network performance to traditional models in financial forecasting.
method Used long short-term memory network for JSE Top 40 return data forecasting.
result Long short-term memory network outperforms seasonal model in forecasting.
Algorithm combines expert forecasts for long-term time series prediction.
problem Long-term time series prediction with expert advice.
method Develops algorithms to combine expert forecasts for long-term prediction, proving adversarial regret bounds.
result Obtains smoothing mechanism to protect against trend changes, noise, and outliers.
AIKAE enhances IKAE for long-term time series forecasting.
problem Limitation of dimension conservation in IKAE models.
method Augmented with a non-invertible encoder network.
result AIKAE improves long-term forecasting accuracy.
FiLM improves deep learning for long-term time series forecasting.
problem Preserving historical information without overfitting noise.
method Applies Legendre Polynomials and Fourier projections, adds low-rank approximation.
result Significantly improves multivariate and univariate forecasting accuracy.
Unified framework for long-range and cold-start seasonal forecasts.
problem Forecasting seasonal profiles with limited historical data.
method Combining high-dimensional regression and matrix factorization.
result Framework accurately forecasts seasonal profiles on multiple datasets.
Paper proposes new method for time series confidence intervals using LSTM.
problem Constructing accurate confidence intervals for multivariate time series.
method Uses Long Short Term Memory Network (LSTM) and novel block bootstrap techniques.
result Demonstrates improved accuracy in constructing confidence intervals.
A deep neural network for spatial time series forecasting.
problem Challenges in forecasting spatial time series with specific patterns and curse of dimensionality.
method Spatial-temporal decomposition, fuzzy clustering, multi-kernel convolution, convolution-LSTM, denoising autoencoder.
result Model outperforms baseline and state-of-the-art models in traffic flow prediction.
FEDformer combines Transformer with seasonal-trend decomposition for efficient long-term forecasting.
problem Transformer's inefficiency and inability to capture global time series views.
method Combines seasonal-trend decomposition with Transformer, exploiting Fourier basis for frequency enhancement.
result Reduces prediction error by 14.8% and 22.6% for multivariate and univariate time series, respectively.
Rough Transformers improve efficiency for medical time-series data.
problem Efficiently modeling irregularly sampled, long-range time-series data.
method Introducing Rough Transformers, a Transformer variant with continuous-time representations and multi-view signature attention.
result Rough Transformers outperform vanilla Transformers while using less computational resources.
New approach shapes error distribution in long-term forecasting.
problem Disparate error distributions in recent transformer models.
method Loss shaping constraints to respect upper bounds on loss at each time-step.
result Competitive average performance with shaped error distribution.
Stacked LSTM networks improve traffic volume forecasting.
problem Accurate traffic volume prediction for better planning.
method Applying stacked Long Short-Term Memory (LSTM) networks for time series forecasting.
result Stacked LSTM networks enhance the accuracy of traffic volume predictions.
RobustSTL decomposes time series robustly to detect anomalies and forecast.
problem Handling complex time series with seasonality fluctuation, trend shifts, and data anomalies.
method RobustSTL uses least absolute deviations regression for trend extraction and non-local seasonal filtering for seasonality extraction.
result RobustSTL outperforms existing solutions in synthetic and real-world time series datasets.
Study finds strong long-range correlations in financial markets, especially over longer time scales.
problem Understanding long-range correlations in limit order book markets.
method Ultra-high frequency order book data from NASDAQ Nordic, detrended fluctuation analysis (DFA).
result Strong evidence of long-range correlation in inter-event durations, becoming stronger over longer time scales.
This review tackles long horizon forecasting in time series analysis using deep learning.
problem Long horizon forecasting in time series analysis.
method Incorporates deep learning techniques such as trend, seasonality, Fourier and wavelet transforms, and various model architectures.
result LHF is an error propagation problem, with models like xLSTM and Triformer showing better performance.
Neural RDEs extend CDEs to irregular time series.
problem Modeling long irregular time series efficiently.
method Representing time series through log-signature and solving RDEs.
result Significant training speed-ups and improved model performance.
DRFormer uses dynamic tokenization and multi-scale transformer to forecast long time series.
problem Forecasting long-term time series data across diverse scales.
method Dynamic tokenizer, multi-scale transformer, dynamic sparse learning, rotary position encoding.
result DRFormer outperforms existing methods in forecasting accuracy.
The paper identifies short-term and long-term time scales in stock markets with and without structural breaks.
problem Understanding the nature of stock markets at short-term and long-term time scales.
method Applied Zivot and Andrews structural trend break model to identify structural breaks. Used empirical mode decomposition and Hurst exponent to analyze time scales.
result Identified short-term and long-term time scales in stock markets, with short-term scales within few days to 3 months and long-term scales greater than 5 months.
A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…
VLSTM improves HFT by handling long sequences in financial trading.
problem Handling long sequences of thousands of data points in high-frequency trading.
method Proposed VLSTM, a variant of LSTM, to address long-term dependencies.
result 3.14\% increase in F1-score over state-of-the-art models.
CaLoNet integrates spatial and local correlations for multivariate time series classification.
problem Ignoring spatial and local correlations in multivariate time series classification.
method Model spatial correlations using causality modeling, extract local correlations, integrate into graph neural network.
result Competitive performance compared to state-of-the-art methods on UEA datasets.
Time-related features improve time series forecasting models.
problem Lack of explicit time-related encoding in current forecasting models limits their ability to capture cyclical and seasonal trends.
method Introducing Time Stamp Forecaster (TimeSter) to encode time-related features and integrating it with a linear backbone.
result TimeLinear model reduces MSE by 23% on benchmark datasets, improving performance with exceptional efficiency.
Extended LSTMs improve volatility prediction by 20%.
problem Predicting asset price volatility with long memory.
method Extended LSTMs with multiple flexible timescales.
result Extended LSTMs outperform rough volatility predictions by 20%.
Proposes a transformer-based approach for anomaly detection in time series data.
problem Inadequate evaluation metrics and inability to capture temporal features in time series anomaly detection.
method Introduces a proper evaluation metric and proposes a transformer-based approach for anomaly detection in time series data.
result Transformer-based approach outperforms state-of-the-art detectors in detecting sequential anomalies.