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Nonstationary time series forecasting using optimized-EVDHM-ARIMA for COVID-19
Frontiers
A forecasting model for nonstationary time series has been created in this paper. The model comprises an optimized EigenValue Decomposition of Hankel Matrix (...
2 months ago
Time-series forecasting through recurrent topology
Nature
Time-series forecasting is a practical goal in many areas of science and engineering. Common approaches for forecasting future events often...
7 months ago
Time Series Forecasting in the Age of GenAI: Make Gradient Boosting Behaves like LLMs
Towards Data Science
We take a step back and try to understand how to carry out zero-shot time-series forecasting with standard machine learning models.
2 months ago
(PDF) Applying k-nearest neighbors to time series forecasting : two new approaches
ResearchGate
In this paper, we introduce two methodologies to forecasting time series that we refer to as Classical Parameters Tuning in Weighted Nearest Neighbors and Fast...
5 months ago
Time series analysis for psychological research: examining and forecasting change
Frontiers
The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the...
7 months ago
(PDF) Forecasting Electricity Consumption Using Time Series Model
ResearchGate
PDF | Electricity demand forecasting is important for planning and facility expansion in the electricity sector. Accurate forecasts can save...
1 month ago
Hybrid time series models with exogenous variable for improved yield forecasting of major Rabi crops in India
Nature
Accurate and in-time prediction of crop yield plays a crucial role in the planning, management, and decision-making processes within the...
8 months ago
Easy and accurate forecasting with AutoGluon-TimeSeries
Amazon Web Services
AutoGluon-TimeSeries is the latest addition to AutoGluon, which helps you easily build powerful time series forecasting models with as little as three lines of...
21 months ago
Interpretable Deep Learning for Time Series Forecasting
Google Research
Multi-horizon forecasting, i.e. predicting variables-of-interest at multiple future time steps, is a crucial challenge in time series...
32 months ago
Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions
BMC Medical Research Methodology
Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions.
41 months ago