EVARS-GPR refines Gaussian Process Regression for seasonal data with sudden scale changes.
problem Challenges in forecasting with changing system behavior over time.
method Combines online change point detection with data augmentation for refitting.
result 20.8% lower RMSE on real-world datasets compared to similar methods.
New bandit algorithm detects and adapts to seasonal changes in rewards.
problem Adapting to abrupt changes in user preferences during events.
method Detects and adapts to seasonal changes in reward function.
result Outperforms state-of-the-art algorithms for non-stationary environments.
Study combines variational inference and transformers for seasonal climate predictions.
problem Lack of robust seasonal predictions due to limited historical records and computational constraints.
method Combines variational inference with transformer models trained on climate model output.
result Method provides skilful predictions beyond climate change-induced trends in various regions.
Modeling daily river flow distribution with seasonal and long-term trends.
problem Capturing both seasonal and gradual long-term changes in environmental variables.
method Distributional regression using GAMLSS framework to estimate daily distribution of river flows.
result Model successfully captures seasonal variation and long-term trends in river flow data.
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…
Recommender system improves with temporal representations.
problem Improving interpretability and performance in recommender systems.
method Incorporates temporal representations via recurrent point process in continuous time.
result Characterizes effects of perception, interest, and seasonal changes on reviews.
Method predicts disease outbreaks using search logs, overcoming instability.
problem Predicting disease outbreaks from search logs is challenging due to short-term and long-term instability.
method Seasonal-adjustment method decomposes logs into seasonal, trend, and irregular components; feature selection method selects relevant search terms.
result Proposed method outperforms comparative methods in prediction accuracy for seven of ten diseases.
Study analyzes seasonal hydroclimatic features across climates and continents.
problem Lack of seasonal hydroclimatic feature analysis for Koppen-Geiger climates and continents.
method Global-scale analysis of 13,000 time series using 7 features.
result Notable differences in feature magnitudes across Koppen-Geiger climate classes and continental regions.
Machine learning predicts seasonal precipitation for East Africa.
problem Predicting seasonal precipitation for East Africa using machine learning.
method Dimension reduction via EOFs, large-scale climate variability indices as features, interpretable ML algorithm.
result The ML approach shows significant positive skill in predicting precipitation for OND season, comparable to ECMWF forecasts.
Seasonal influenza infects between 10 and 50 million people in the United States every year, overburdening hospitals during weeks of peak incidence. Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza-like illness (ILI) forewarn public he…
Novel algorithm SAODE improves high-dimensional stream classification in seasonal data.
problem Handling seasonal concept drift in high-dimensional stream classification.
method SAODE classifier that includes time as a super parent to handle seasonal drift.
result SAODE consistently outperforms other methods in stream and concept drift classification.
Proposes a method for forecasting time series with multiple seasonality.
problem Forecasting time series with both short-term and long-term seasonality is challenging.
method Two-stage method: first generalizes ARMA model for multiple seasonality, second selects lag order.
result Method outperforms `Facebook Prophet` model in predictive performance.
Study improves seasonal forecasts using deep learning.
problem Challenges in generating large forecast ensembles and limited observations for verification.
method Developed a probabilistic deep neural network model.
result Demonstrated favorable skill compared to state-of-the-art dynamical forecast systems.
A new framework detects changepoints in complex data.
problem Detecting structural changes in data with various patterns and trends.
method Iteratively Reweighted Fused Lasso (IRFL) for L0 model selection.
result IRFL achieves accurate changepoint detection across various challenging scenarios.
Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the disti…
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership. Although successful task-specific methods have been developed for some label changing applications, to da…
We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U.S. stocks. We show that, over short time intervals, price changes are mainly driven by the order flow imbalance, defined as the imbalance between supply and demand at the best bid and ask pri…
This paper examines the intra-day seasonality of transacted limit and market orders in the DEM/USD foreign exchange market. Empirical analysis of completed transactions data based on the Dealing 2000-2 electronic inter-dealer broking system indicates significant evidence of intraday seasonality in returns and return vo…
We introduce a multi-factor stochastic volatility model for commodities that incorporates seasonality and the Samuelson effect. Conditions on the seasonal term under which the corresponding volatility factor is well-defined are given, and five different specifications of the seasonality pattern are proposed. We calcula…
We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it does oft…
We introduce a multi-factor stochastic volatility model based on the CIR/Heston volatility process that incorporates seasonality and the Samuelson effect. First, we give conditions on the seasonal term under which the corresponding volatility factor is well-defined. These conditions appear to be rather mild. Second, we…
Study identifies transitions between traffic modes on Cologne motorways.
problem Understanding transitions between different traffic modes.
method Constructed state transition network, identified dominant states using PageRank algorithm.
result Identified seasonal dependence in traffic modes.
Study analyzes European energy markets' reactions to 2022 events using Bayesian methods.
problem Detecting structural breakpoints in energy and financial markets during turbulent times.
method Combines Hurst exponent for market efficiency, BEAST for abrupt changes, and seasonal/trend analysis.
result Markets exhibit varied reactions to critical events, affecting their trends and breakpoints.
New model detects anomalies robustly in noisy, seasonal multivariate time series.
problem Detecting anomalies in noisy, seasonal multivariate time series data.
method Proposes Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN).
result Improves robustness and precision in detecting anomalies.
Robust algorithm detects season length without parameters.
problem Automatic detection of season length in time series.
method Interpolating, filtering, detrending followed by autocorrelation analysis.
result Outperforms existing algorithms in practical applications.
This paper studies subordinate Ornstein-Uhlenbeck (OU) processes, i.e., OU diffusions time changed by Lévy subordinators. We construct their sample path decomposition, show that they possess mean-reverting jumps, study their equivalent measure transformations, and the spectral representation of their transition semigro…
MQTransformer improves forecast accuracy with context-aware attention.
problem Improving probabilistic demand prediction accuracy.
method Incorporates Transformer architectures for context alignment and feedback-aware attention.
result Significant improvements in forecast accuracy, reducing excess variability.
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these contexts. In this paper, we propose Long Short-Term Memory Multi-Seasonal Net…
ST-MTM models complex time series by decomposing and masking seasonal and trend components.
problem Forecasting complex time series with intricate temporal variations.
method Seasonal-Trend Decomposition with Masking and Contrastive Learning.
result ST-MTM achieves superior forecasting performance compared to existing methods.
Deep learning models perform variably across continents/seasons in land cover mapping.
problem Variability in deep learning model performance across different continents/seasons.
method Clustering techniques on satellite imagery from different continents.
result Model performance varies significantly between different continents/seasons.
Developed deep learning models to predict crop yields across diverse environments.
problem Precise crop yield prediction for improved agricultural practices and climate resilience.
method Integrated weather data, used CNN-DNN and CNN-LSTM-DNN models, applied GEM method.
result Achieved superior performance in crop yield prediction with reduced RMSE and MAE.
New method reduces regret in nonparametric bandits with unknown covariate shifts.
problem Optimal actions depend on context, but context distributions can change over time.
method Derives new regret bounds for nonparametric bandits under covariate shifts.
result Regret bounds adaptively attainable without knowledge of shift time or magnitude.
A robust algorithm for forecasting vector time series with seasonal components.
problem Forecasting vector time series with seasonal patterns and handling missing data.
method Auto-regression with seasonal annual, weekly, and daily baselines, and a Gaussian process for residuals. Custom truncated eigendecomposition and low-rank plus block-diagonal Gaussian kernel. Schur complement and Tikhonov regularization for efficient inference.
result The model can scale to very large datasets and is efficient in terms of memory and computation.
RSM-GAN detects anomalies in noisy, multivariate, seasonal time series.
problem Anomaly detection in complex, noisy multivariate time series with seasonality.
method Convolutional-LSTM layers and attention mechanism in a GAN framework.
result RSM-GAN outperforms existing models in false positive rate and precision.
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.
Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Expe…
Robust PCA detects anomalies and fills gaps in seasonal time series data.
problem Anomaly detection and data imputation in seasonal time series.
method Online robust PCA framework for temporal observations.
result Empirically compared and showed effectiveness in practical situations.
Advanced forecasting models outperform Holt-Winters and ARIMA for stock market data.
problem Forecasting stock market data with improved accuracy.
method Developed 24 two-parameter families of forecasting functions using a nonparametric approach.
result Our models outperform Holt-Winters and ARIMA in terms of lower sum of absolute errors and higher number of accurate forecasts.
Develops a nonparametric framework for detecting changes in sequential data.
problem Detecting changes in nonparametrically specified distributions.
method Introduces e-detectors based on e-processes for nonnegative supermartingales.
result Provides bounds on average run length and detection delay.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
Recent advances in the understanding of time series permit to clarify seasonalities and cycles, which might be rather obscure in today's literature. A theorem due to P. Cartier and Y. Perrin, which was published only recently, in 1995, and several time scales yield, perhaps for the first time, a clear-cut definition of…
Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data
problem Improving prediction intervals for non-exchangeable time-indexed datasets
method Spectral adaptive conformal prediction
result Improves on fixed spectral weighting while monitoring uncertainty changes
Novel approach predicts long-term seasonal component of electricity prices for improved forecasting.
problem Improving day-ahead electricity price forecasting accuracy.
method Extracts trend-seasonal pattern from extrapolated price series using autoregressive and LASSO models.
result Improves predictive accuracy by 3-15% in root mean squared error and 1% in profits.
Gold prices show seasonal behavior, with January and July having opposite returns.
problem Seasonal behavior in gold prices during the turn of the year.
method Statistical analysis and decomposition techniques.
result Gold prices exhibit strong cyclical behavior during the turn-of-the-year period, with January showing the highest return and July showing significant negative returns.
Enhanced time series forecasting with improved trend and seasonal components.
problem Challenges in real-world time series forecasting, especially in multivariate applications.
method Individual decomposition of trend and seasonal components, using different approaches for each.
result Significant reduction in error values, around 10% MSE average reduction across benchmarks.
New method reconstructs past foehn occurrences using unsupervised and supervised learning.
problem Reconstructing past foehn occurrences due to lack of direct measurement.
method Combining unsupervised and supervised learning methods to infer foehn occurrences from reanalysis data.
result Accurate hourly reconstructions of past foehn occurrences for 83 years.
Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has beco…
Improved electrical load forecasting model using Fourier-enhanced RNN.
problem Electrical load time series downscaling with high accuracy and low error.
method Combines recurrent neural network with Fourier seasonal embeddings and self-attention.
result Significantly reduces RMSE across different time horizons compared to existing methods.