luci-app-fleth は、IPv4 over IPv6トンネル（DS-Lite MAP-E）を自動設定ヘルパです。

Distributed and decentralized MAPE-K loops framework

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.

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.

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 ...

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 predict…

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.

Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.

Compute a moving arctangent mean absolute percentage error (MAAPE) incrementally.

This is an linear approach machine learning model used to predict the values of variable(dependent) based on other variables(independent).

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 …

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 Exponenti…

This advanced forecasting tool leverages Prophet, ARIMA, SARIMA, and LSTM models to predict daily sales for 32 pizzas and 64 ingredients. With Prophet achieving the lowest MAPE, it ensures accurate demand forecasts, optimized inventory, and efficient purchase planning, reducing waste, preventing stockouts, and enhancing supply chain efficiency.

R code for exchange rate prediction using Multilayer Perceptron (MLP) models with various architectures and evaluation metrics

資料科學的日常研究議題

Compute the mean absolute percentage error (MAPE) incrementally.

Compute a moving mean absolute percentage error (MAPE) incrementally.

Compute the mean arctangent absolute percentage error (MAAPE) incrementally.