Search Results for "forecasting"

darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account.

...RCFs were originally developed at Amazon to use in a nonparametric anomaly detection algorithm for streaming data. Later new algorithms based on RCFs were developed for density estimation, imputation, and forecasting. The different directories correspond to equivalent implementations in different languages, and bindings to to those base implementations, using language-specific features for greater flexibility of use.

Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock prices is a complex task, as it is influenced by various factors such as market trends, political events, and economic indicators. ...

With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that...

The applied-ml repository is a rich, curated collection of papers, technical articles, and case-study blog posts about how machine learning (ML) and data-driven systems are applied in real production environments by major companies. Instead of focusing solely on theoretical ML research, this repo highlights industry-scale challenges: data collection, quality, infrastructure, feature stores, model serving, monitoring, scalability, and how ML is embedded in product workflows. It acts as a...

TRANSIMS is an integrated system of travel forecasting tools for modeling regional transport systems. For more information, visit http://code.google.com/p/transims/

Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. ...

...Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. Its applicable objects include quantitative traders, teachers, and students majoring in finance, people interested in economic data, programmers, and people who like freedom and the spirit of exploration. You could write the Strategy using an event-driven or time walkway and view and analyze the performance in a uniform way.

#ESP.Meteo: Solar Wi-Fi weather station on ESP12 + ionistor + solar panel Current Functionality: *Forecasting the probability of frosts (according to the methods of Professor Brounov) *Forecast of the height of the lower edge of the cloud (VNGO by Selezneva) *Calculation of the height of the sensor according to the barometric formula (altimeter, for other projects) *Measurement of street temperature *Measurement of outdoor humidity *Measurement of street pressure *Calculation of the dew point *Monitoring of voltage on ionistors (charge-discharge control) *Monitoring the opening of the box flap with potatoes *Monitoring the system time-out every 5 minutes (operating time ~ 480 ms) *Monitoring the temperature on the balcony (the ability to connect up to 16 sensors DS18B20: 2 groups of 8 sensors per two APIKEY) *Sending data to the Thingspeak cloud *Sending data to Twitter

OpenForecast is a package of general purpose, forecasting models written in Java that can be applied to any data series. No knowledge of forecasting is required. Using this package, the most appropriate algorithm for your data will be selected.

An Eclipse plug-in to support individuals using high-maturity, metrics-intensive in PSP(SM) - Personal Software Process. Supports personalearned value tracking and forecasting; simplifies metrics collection and analysis.