Browse > Time Series > Time Series Forecasting

Time Series Forecasting

22 papers with code ยท Time Series

Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).

State-of-the-art leaderboards

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Latest papers without code

Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning

17 Sep 2019

A robust model for time series forecasting is highly important in many domains, including but not limited to financial forecast, air temperature and electricity consumption.

MULTI-TASK LEARNING MULTI-VIEW LEARNING TIME SERIES TIME SERIES FORECASTING

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

9 Sep 2019

With rapid adoption of deep learning in high-regret applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties.

CALIBRATION OBJECT LOCALIZATION TIME SERIES TIME SERIES FORECASTING

Recovery of Future Data via Convolution Nuclear Norm Minimization

6 Sep 2019

This paper is about recovering the unseen future data from a given sequence of historical samples, so called as \emph{future data recovery}---a significant problem closely related to time series forecasting.

TIME SERIES TIME SERIES FORECASTING

Causally interpretable multi-step time series forecasting: A new machine learning approach using simulated differential equations

27 Aug 2019

This work represents a new approach which generates then analyzes a highly non linear complex system of differential equations to do interpretable time series forecasting at a high level of accuracy.

TIME SERIES TIME SERIES FORECASTING

The Wiki Music dataset: A tool for computational analysis of popular music

27 Aug 2019

Is it possible use algorithms to find trends in the history of popular music?

TIME SERIES TIME SERIES FORECASTING

Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms

22 Aug 2019

Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inputs for meta-learners; evaluating the effect of forecasting error measures; and assessing the role of the dimensionality of the feature space on the forecasting errors of meta-learners.

DIMENSIONALITY REDUCTION FEATURE SELECTION META-LEARNING MODEL SELECTION RECOMMENDATION SYSTEMS TIME SERIES TIME SERIES ANALYSIS TIME SERIES FORECASTING

Mixed pooling of seasonality in time series pallet forecasting

14 Aug 2019

Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of each period in seasonal patterns, such as the unique behavior of specific days of the week in business data.

TIME SERIES TIME SERIES FORECASTING

Deep Generative Quantile-Copula Models for Probabilistic Forecasting

24 Jul 2019

We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation.

TIME SERIES TIME SERIES FORECASTING

A machine learning framework for computationally expensive transient models

12 Jul 2019

The promise of machine learning has been explored in a variety of scientific disciplines in the last few years, however, its application on first-principles based computationally expensive tools is still in nascent stage.

TIME SERIES TIME SERIES FORECASTING

Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting

1 Jul 2019

Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry.

TIME SERIES TIME SERIES FORECASTING