Open Source Python Budgeting Software

PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots. Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box.

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Prophet is used in many applications across Facebook for producing reliable forecasts for planning and goal setting. We’ve found it to perform better than any other approach in the majority of cases. We fit models in Stan so that you get forecasts in just a few seconds. Get a reasonable forecast on messy data with no manual effort. Prophet is robust to outliers, missing data, and dramatic changes in your time series.

Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. Currently, Task-TS from CoronaWhy primarily maintains this repository. Pull requests are welcome. Historically, this repository provided open-source benchmarks and codes for flash flood and river flow forecasting. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference.

Budse is a simple budgeting tool that implements bucket (or envelope) budgeting. Currently there is a Command Line Interface (CLI) with a Graphical User Interface in progress. The program includes extras such as transaction searching and reports.

Simple personal program to manage your spending. No accounting experience required.

eele es un editor de cuentas generales en formato XML para las entidades locales españolas obligadas a la rendición telemática de cuentas mediante la plataforma http://rendiciondecuentas.es.

a personal money manager that handles bank accounts and keeps track of bills. It also provides a function to set aside money from an account for a particular purpose, ie grocery money for the week.

NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in doubt. We are working on an improved documentation. We appreciate any help to improve and update the docs. Lagged regressors (measured features, e.g temperature sensor). Future regressors (in advance known features, e.g. temperature forecast). Country holidays & recurring special events. Sparsity of coefficients through regularization. Plotting for forecast components, model coefficients as well as final predictions. Automatic selection of training-related hyperparameters.

Management and Accounting software for small and French-like establishment.

A small program in python developed to simulate poker bankroll growth over time depending on money and risk management strategies used. No GUI, user must edit parameters in source file. Outputs to .csv format.

Tracking of hours, goods, project productivity. Easy to use interfaces, Daily reports of what's been done, and what needs doing. If additions need to be made very modular and easy to add new features for job specifics.

PyOrganizer is a cross-platform (Python/GTK) personal organizer, with a GUI optimized for the Nokia Internet Tablets (n810, n800, and n700). The financial/budgeting features are now operational, and rudimentary mileage tracking has just been added.

SD-Buy is a framework made for building an application for buyers. On companies, the service purchase does not take into account the environmental aspect. Today, this point is important (and mandatory in France) but there is no tool for this.

It helps you tracking your scheduled bills or salary or whatever, recursive or not. In a nutshell, it helps to remember and analyze the budget.

Snidget is a python program which tracks your financial information, in particular your expenses and income. The user adds each transaction on their accounts and snidget can provide reports about how much you're spending. Includes both CLI and GUI.