eZforecast¶

A python library for time-series forecasting with easy manipulation of modern algorithms and techniques, this work focus on production-ready quality.

Data Loader

read_csv: Loading time-series from csv file.
read_json: Loading time-series from json file with pre-define schema.
read_rdbms: Loading time-series from relational database management sytem with pre-define connection and schema.
read_bigquery: Loading time-series from Google Cloud BigQuery.
read_readshift: Loading time-series from Amazon RedShift.
read_snowflake: Loading time-series from Snowflake.

Data Processor

Model

Arima: Auto-Regressive Integrated Moving Average.
LSTM: Long Short Term Memory.
NBeats: Neural basis expansion analysis for interpretable time series forecasting.
ConditionalNormalizingFlowLSTM: Conditional Normalizing Flows LSTM.
TimescaleLSTM: Timescale LSTM.
ODELSTM: Ordinary differential equations with LSTM.
ESRNN: Exponential smoothing - RNN.
ES: Simple exponential smoothing.
MTGNN: Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks.
GNNLSTM: GNN - LSTM.
Z-GCNET: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
LiquidTimeconstantNetwork: Liquid Time-constant Networks.
GRUODEBayes: GRU-ODE-Bayes.

Loss

Metric

Trainer

Optimizer

Deployment