luci-proto-fleth は、IPv4 over IPv6トンネル（DS-Lite/MAP-E/IPIP6）の自動設定ヘルパです。

A hands-on project for forecasting time-series with PyTorch LSTMs. It creates realistic daily data (trend, seasonality, events, noise), prepares it with sliding windows, and trains an LSTM to make multi-step predictions. The project tracks errors with RMSE, MAE, MAPE and shows clear plots of training progress and forecast results.

Open reproducible benchmarks for food-image recognition models and APIs.

Distributed and decentralized MAPE-K loops framework

資料科學的日常研究議題

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…

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.

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

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.

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 ...

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.

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.

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

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 …

🎬 This repository focuses on building a personalized movie recommender system for ZEE5 using collaborative filtering, content-based methods, and matrix factorization, Pearson correlation, Suprise SVD techniques to deliver accurate, user-relevant recommendations & evaluated through NDCG, MRR, MAPE

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

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…

Predicting Walmart Sales and Performing Exploratory Data Analysis

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