When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
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18 results for “Prophet”
Retail company uses Prophet algorithm for accurate sales forecasting.
problem Accurate sales forecasting in the retail industry.
method Facebook's Prophet algorithm and backtesting strategy.
result Framework demonstrates real-world use case capabilities.
NeuralProphet improves forecast accuracy by 55-92% for short-term forecasts.
problem Challenges in explainable, scalable forecasting for business decisions.
method Hybrid framework combining classical methods and deep learning, with auto-regression and covariate modules.
result NeuralProphet outperforms Prophet on real-world datasets and short-term forecasts.
Optimal learning for parametric prophet inequalities with exponential-type distributions
problem Learning in prophet inequalities with unknown parameters
method Confidence-based dynamic-programming policy
result Achieves optimal asymptotic competitive ratio using online observations
This study enhances sales forecasts by integrating market indicators into forecasting models.
problem Traditional forecasting models rely solely on historical demand data.
method Automated integration of macroeconomic time series data (GDP growth) into forecasting models using feature selection methods.
result Feature selection methods, especially Forward Feature Selection, significantly improve forecasting accuracy.
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
problem Forecasting volatile retail sales trends with unknown factors.
method Benchmarked traditional ARIMA model against advanced models like Prophet and LightGBM on historical Walmart sales data.
result ARIMA model outperforms LightGBM and achieves computational efficiency.
Study noisy rewards in online decision-making with unknown distributions.
problem Learning optimal decisions in online settings with noisy and unknown reward distributions.
method Proposes algorithms integrating learning and decision-making via LCB thresholding.
result Achieves competitive ratios of 1 - 1/e and 1/2 in various settings.
Prophet predicts device qualities for FL to reduce training latency.
problem Bad candidate-selection leads to large training and reporting latency in FL.
method Each device predicts its own training and reporting phases using LSTM. The algorithm is implemented with DRL.
result The proposed approach outperforms reactive algorithms in real-world experiments.
Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.
problem Accurate forecasting of urban air pollution for public health and policy guidance.
method Investigated lightweight additive models (FBP, NP) vs. deep learning and hybrid pipelines on Beijing PM2.5 and PM10 data.
result Facebook Prophet consistently outperformed NeuralProphet and traditional models, achieving high values.
Paper presents forecasting models for platelet demand.
problem Managing platelet demand and supply is challenging due to variability and short shelf life.
method Utilized ARIMA, Prophet, lasso regression, and LSTM networks on a clinical dataset.
result Multivariate approaches generally have higher accuracy, but simpler ARIMA can suffice with sufficient data.
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.
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.
New algorithms improve time series prediction with uncertainty quantification.
problem Uncertainty quantification for time series prediction.
method Combines conformal prediction and control theory for online, adaptive forecasting.
result Improves coverage over ensemble forecasters in real-world applications.
The paper compares advanced deep learning models for Indian stock price forecasting.
problem Complexity of stock price forecasting due to numerous influencing factors.
method Utilizes historical data from national banks in India, combines deep learning models and sentiment analysis.
result Achieved higher accuracy in stock price forecasting compared to traditional methods.
Proposes a new TS algorithm for non-stationary bandits using KS tests.
problem Non-stationary multi-armed bandit problems.
method Active detection of change points using KS tests and adaptive Thompson Sampling.
result Sub-linear regret demonstrated for the two-armed bandit case.
Study forecasts COVID-19 cases in Iran using deep learning.
problem Predicting the spread of COVID-19 in Iran.
method Deep learning models (Prophet, LSTM, AR, ARIMA) for forecasting.
result Deep learning models outperform time series forecasting.
LSTM Networks accurately forecast COVID-19 cases in Turkey with lower error than other methods.
problem Forecasting total COVID-19 cases in Turkey using machine learning.
method Long Short-Term Memory (LSTM) Networks for forecasting.
result LSTM Networks outperform other methods in forecasting accuracy.
New method combines simple forecasting techniques for river flow predictions.
problem Improving accuracy of long-term hydrological forecasts.
method Combines at least two forecasting methods using median combiner.
result Performs well in long-term forecasts, especially with multiple methods.