StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.
Automated Bayesian model discovery for time series data
StateSpaceLearning.jl is a Julia package for time-series analysis using state space learning framework.
Julia Package with SARIMA model implementation using JuMP.
Collection of plot functionalities for time series analysis.
Julia package to generate, estimate, and forecast long memory processes
Julia package for simulating and estimating multi-level/hierarchical dynamic factor models (HDFMs).
Forecasting using Arar algorithm
Toolkit functions and example outputs for Bayesian (Structural) Vector Autoregressive (VAR) models
Modelling to investigate how imperfect foresight and information affects storage operation. Case studies using data from the Australian National Electricity Market and the Australian Energy Market Operator
Automatic multi-seasonal ARIMA Learning
IntermittentDemand.jl: Intermittent demand forecasting in Julia Language, |forecasting|Julia|
Bot for downloading Solar irradiance data at target locations and region surrounded it.
Forecasting using a combinatoric approach and exploiting parallel computing in Julia.
Julia implementation of Déjà vu: A data-centric forecasting approach through time series cross-similarity
AirBorne a complete algorithmic trading framework in Julia.
the Altman's z score is a Solvency Predictor, was once known as a 'good' financial predictor of a company's solvency, based on logical Common Sense & Accounting Ratios
Tools for estimating BVAR models, producing forecasts, and reporting results. Based on the review in Karlsson (2013).
Provides methods for dynamic factor models, such as estimation, w/ and w/o penalization, forecasting and filtering
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