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.
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.
Deep learning models predict call center volumes with seasonal patterns.
problem Forecasting call center volumes with complex seasonal behavior.
method Investigated recurrent neural networks (RNNs) including Elman, LSTM, and GRU models.
result Optimal RNN configurations outperform other forecasting techniques.
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.
In this paper, in following of the first part (which ADF tests using ACI evaluation) has conducted, Time Series (TSs) are analyzed using decomposition analysis. In fact, TSs are composed of four components including trend (long term behavior or progression of series), cyclic component (non-periodic fluctuation behavior…
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.
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…
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 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…
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…
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.
Method detects multi-timescale consumer spending patterns from receipts.
problem Understanding and managing consumer behavior in high-dimensional data.
method Non-negative tensor factorization (NTF) to extract multi-timescale expenditure patterns.
result Consumption patterns are characterized based on spending behavior over different timescales.
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.
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.
The abstract discusses open data resources for studying and controlling the spread of COVID-19.
problem Understanding and controlling the spread of COVID-19.
method Identification and description of open data resources and data-driven methodologies.
result Identification of variables and open data resources for analyzing COVID-19.
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.
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.
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.
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.
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.
Current auto loans converge to super-prime credit despite remaining underwater.
problem Inefficient consumer behavior in auto loans leading to suboptimal credit risk.
method Large-sample statistical hypothesis test on transition matrix between risk bands.
result All current risk bands converge to super-prime credit, despite remaining underwater.
Ebay uses forecasting and simulation to decide when to disable a vendor.
problem Determining the optimal time to disable a vendor to avoid customer loss.
method Data-driven approach involving multiplicative seasonality model, Monte Carlo simulation, and linear model.
result Identifies the best time to disable a vendor to minimize customer loss.
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…
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.
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.
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.
New method corrects seasonal Arctic sea ice predictions with probabilistic models.
problem Systematic biases and errors in climate model forecasts of Arctic sea ice.
method Conditional Variational Autoencoder model to map observation distribution given biased model predictions.
result Probabilistic adjusted forecasts are better calibrated and have smaller errors.
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…
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 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%.
Machine learning improves sub-seasonal climate forecasting, especially gradient boosting and deep learning.
problem Predicting climate variables like temperature and precipitation in 2-week to 2-month time scales.
method Carefully constructed feature representations and ML approaches including gradient boosting and deep learning.
result ML methods can outperform climatological baselines and improve prediction accuracy.
Study financial markets using synchronization measures and clustering algorithms.
problem Analyze high-frequency trading dynamics and market states.
method Ordinal pattern series, information-theoretic synchronization measure, clustering algorithms, Markov model.
result Identify two coherent seasons of centralized and decentralized synchronicity.
Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.
problem Causal inference in non-stationary, autocorrelated time series data.
method Decomposes time series into trend, seasonal, and residual components; performs component-specific causal analysis.
result Framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, especially under strong non-stationarity and temporal autocorrelation.
Study finds significant price declines and capital reallocation from centralized to decentralized exchanges after FTX collapse.
problem Quantifying trust dynamics and redistribution between centralized and decentralized exchanges.
method Interdisciplinary approach combining causal inference and computational text analysis.
result Significant price declines and capital reallocation from centralized to decentralized exchanges following the FTX collapse.
Study uses TDA to improve OEE forecasting in manufacturing.
problem Volatility and nonlinearity in OEE time series data.
method Topological Data Analysis (TDA) to transform raw data into structured knowledge.
result Improves OEE forecasting accuracy by at least 17%.
A novel algorithm predicts customized allergy seasons using multi-variate triple-regression.
problem Predicting customized allergy seasons for individual patients.
method Triple-regression algorithm with pre-processing and three-stage regressions.
result Improved forecasting accuracy and reduced uncertainty.
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.
In this paper we perform a statistical analysis over the returns and relative prices of the CAC 40 and the S\&P 500 with the purpose of analyzing the intra-day seasonalities of single and cross-sectional stock dynamics. In order to do that, we characterized the dynamics of a stock (or a set of stocks) by the evolut…