Topic: mape Goto Github
Some thing interesting about mape
Some thing interesting about mape
mape,This is an linear approach machine learning model used to predict the values of variable(dependent) based on other variables(independent).
User: akashash01
mape,Electric Load forecasting for a year on hourly basis using 3 different techniques. - linear Regression, - ANN (Using Matlab nntool), -K-Nearest Neighbor. All 3 codes are present with an detailed report on each technique.
User: aniket-thopte
mape,Basic to complex prediction model using exhaustive selector & Lasso
User: archan311
mape,Sales forecasting is an essential task for the management of a store. Machine learning can help us discover the factors that influence sales in a retail store and estimate the number of sales in the near future.
User: asim5800
mape,Project to predict production quantities for a given dataset using Machine Learning algorithms.
User: ayushegangal
mape,Using MS Excel and R, accurately forecasted total core deposit data from a Richmond Bank. The Holt’s Linear Exponential Smoothing had the overall lowest “Quick and Dirty” MAPE (1.2%), the lowest overall Maximum MAPE (3.49%), and consistently more accurate projections for each of the forecast horizons. Overall, the Unaided, Holts Linear Exponential Smoothing, and both regressions overestimated while the Naïve, 12 Month (M) Center Moving Average (CMA), 3M Moving Average (MA), 6M MA, Damped Trend Exponential Smoothing, and Simple Exponential Smoothing underestimated.
User: bryce-bowles
mape,Swarm intelligence aims at exploring the complicated relationships among multi-agents to stimulate co-evolution and the emergence of intelligent decision-making. Based on Multi-agent Particle Environment and deep Reinforcement learning method, we propose ...
User: edision-liu
mape,Distributed and decentralized MAPE-K loops framework
User: elbowz
Home Page: https://elbowz.github.io/PyMAPE/
mape,Implementation of a simple linear regression with single feature
User: eutienne
mape,Meta Apes is the all encompassing reward token, staking platform, and NFT ecosystem. Holding MAPES tokens allow you to receive 7% rewards in any BEP20 token of your choosing.
Organization: meta-apes
mape,in this repository we intend to predict Google and Apple Stock Prices Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predicting stock prices.
User: mohammad-heydariii
mape,Sober truths: Predict the number of fatalities and alcohol-impaired driving crashes
User: nirajsaran2
mape,Recommendation system to predict movie rating given by user on Netflix.
User: pranshu1921
mape,BI Master - Trabalho final da disciplina de Redes Neurais - Redes recorrentes LSTM, GRU. Métricas de avaliação RMSE, MSE, MAPE e MAE.
User: rrfsantos
mape,This repository has the implementation of Performance Metrics (e.g. F1 score, AUC, Accuracy, etc) from scratch, without using Scikit Learn library.
User: sachelsout
mape,Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.
User: shubhammandhare10
mape,Compute the mean arctangent absolute percentage error (MAAPE) incrementally.
Organization: stdlib-js
Home Page: https://github.com/stdlib-js/stdlib
mape,Compute the mean absolute percentage error (MAPE) incrementally.
Organization: stdlib-js
Home Page: https://github.com/stdlib-js/stdlib
mape,Compute a moving arctangent mean absolute percentage error (MAAPE) incrementally.
Organization: stdlib-js
Home Page: https://github.com/stdlib-js/stdlib
mape,Compute a moving mean absolute percentage error (MAPE) incrementally.
Organization: stdlib-js
Home Page: https://github.com/stdlib-js/stdlib
mape,Splitting data, Moving Average, Time series decomposition plot, ACF plots and PACF plots, Evaluation Metric MAPE, Simple Exponential Method, Holt method, Holts winter exponential smoothing with additive seasonality and additive trend, Holts winter exponential smoothing with multiplicative seasonality and additive trend, Final Model by combining train and test
User: vaitybharati
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