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TimeMixer: Exploring the Latest Model in Time Series Forecasting | by Marco Peixeiro | Jul, 2024
Towards Data Science
Discover and understand the inner workings of TimeMixer and apply it in your own forecasting project using Python.
1 week ago
Improving long-term multivariate time series forecasting with a seasonal-trend decomposition-based 2-dimensional temporal convolution dense network
Nature
Improving the accuracy of long-term multivariate time series forecasting is important for practical applications.
6 months ago
(PDF) Bitcoin Price Forecasting: A Comparative Study of Machine Learning, Statistical and Deep Learning Models
ResearchGate
PDF | On Apr 25, 2024, Neelam Urooj and others published Bitcoin Price Forecasting: A Comparative Study of Machine Learning, Statistical and...
2 months ago
Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam
Nature
The aims of this study are to predict the stock price trend in the stock market in an emerging economy. Using the Long Short Term Memory...
4 months ago
A case study using machine learning and other methods applied to time series data
ResearchGate
A case study using machine learning and other methods applied to time series data. Victor Manuel Piedrafita Acin. Final projects. Fall 2023.
6 months ago
(PDF) Forecasting The Consumer Price Index: A Comparative Study of Machine Learning Methods
ResearchGate
PDF | The Consumer Price Index (CPI) is an indicator of inflation and is tracked by many government and economic agencies to make decisions...
5 months ago
concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data
Nature
Carbon Dioxide (CO $$_{2}$$ ) is a significant contributor to greenhouse gas emissions and one of the main drivers behind global warming and...
9 months ago
Advances in Deep Learning for Time Series Forecasting and Classification: Winter 2023 Edition
Towards Data Science
It has been quite sometime since I've written an update on the state of deep learning for time series. Several conferences have come and...
18 months ago
A DEEP LEARNING APPROACH USING LONG SHORT-TERM MEMORY
ResearchGate
PDF | Rainfall is a significant climatic parameter that is often used as a crucial input for hydrological analysis and modeling for the...
4 months ago
A novel bidirectional LSTM deep learning approach for COVID-19 forecasting | Scientific Reports
Nature
COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed...
9 months ago