arXiv research

TiDE uses MLP for fast, simple long-term time-series forecasting.

MPPN network improves long-term time series forecasting accuracy.

This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.

fSDE-Net generates time series with long-term memory using neural networks.

Introduces Spectral Attention for better long-range time series forecasting.

TimeBridge addresses non-stationarity in long-term time series forecasting.

Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, $u_i$, can be detected and quantified by studying the correlations in the magnitude series $|u_i|$, i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact …

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…

W-Transformers use wavelets to improve time series forecasting.

Combines CNN and Transformer for financial time series forecasting.

Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series.…

Preformer improves Transformer for long-term time series forecasting.

PureTS uses simple linear models to improve long-term time series forecasting.

Transformers improve time series modeling by capturing long-range dependencies.

KEDformer improves long-term time series forecasting with seasonal-trend decomposition.

Timer-XL predicts multidimensional time series using a unified Transformer approach.

New RNN model handles long-term dependencies in irregularly-sampled time series.

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…

TSLANet improves time series models by capturing long-term and short-term interactions.

Improved NODEs for long-term time series forecasting.

FEDformer combines Transformer with seasonal-trend decomposition for efficient long-term forecasting.

AIKAE enhances IKAE for long-term time series forecasting.

In this work we propose a new class of long-memory models with time-varying fractional parameter. In particular, the dynamics of the long-memory coefficient, $d$, is specified through a stochastic recurrence equation driven by the score of the predictive likelihood, as suggested by Creal et al. (2013) and Harvey (2013)…

The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time ser…

Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold-start problem seen in recommender systems which…

Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series. This paper uses an adaptive spectral technique which jointly models the non-stationarity and dependency of financial time series in a non-parametric fashion assuming that the time…

We examine the scaling regime for the detrended fluctuation analysis (DFA) - the most popular method used to detect the presence of long memory in data and the fractal structure of time series. First, the scaling range for DFA is studied for uncorrelated data as a function of length $L$ of time series and regression li…

Long-range correlation and fluctuation in the gold market time series of world's two leading gold consuming countries, namely China and India, are studied. For both the market series during the period 1985-2013 we observe a long-range persistence of memory in the sequences of maxima (minima) of returns in successive ti…

Paper proposes new method for time series confidence intervals using LSTM.

New approach shapes error distribution in long-term forecasting.

Rough Transformers improve efficiency for medical time-series data.

Spatial time series forecasting problems arise in a broad range of applications, such as environmental and transportation problems. These problems are challenging because of the existence of specific spatial, short-term and long-term patterns, and the curse of dimensionality. In this paper, we propose a deep neural net…

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properl…

This review tackles long horizon forecasting in time series analysis using deep learning.

Time-related features improve time series forecasting models.

Long short-term memory network outperforms seasonal model in JSE Top 40 forecasting.

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…

FiLM improves deep learning for long-term time series forecasting.

Neural RDEs extend CDEs to irregular time series.

Stacked LSTM networks improve traffic volume forecasting.

In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks. We focus on time-series with long-range dependencies, needed for monitoring fine granularity data (e.g. minutes, seconds, milliseconds), prevalent in operational use-cases. Tra…

Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our …

DRFormer uses dynamic tokenization and multi-scale transformer to forecast long time series.

LLapDiff models irregular multivariate time series without step-by-step integration.

Study uses IMFs and neural networks to predict economic time series, enhancing interpretability.

It is generally accepted that many time series of practical interest exhibit strong dependence, i.e., long memory. For such series, the sample autocorrelations decay slowly and log-log periodogram plots indicate a straight-line relationship. This necessitates a class of models for describing such behavior. A popular cl…

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve based mod…

We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is perform…