Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

Simple python example on how to use ARIMA models to analyze and predict time series.

Timeseries for everyone

Time Series Analysis and Forecasting in Python

基于ARIMA时间序列的销量预测模型，实际预测准确率达90%以上，内含有测试记录和实际上线效果。

Projetos de modelagem e previsão de séries temporal em linguagem Python e linguagem R. Usarei vários modelos de bibliotecas e pacotes usados para tratamento, modelagem e previsão de séries temporais. Falarei um pouco sobre cada uma delas, gerarei a validação e as previsões e, por fim, realizarei a avaliação com a métricas pertinentes.

Jupyter Notebooks Collection for Learning Time Series Models

Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models

Performed time series analysis using ARIMA model in python on online retail dataset.

Creating a model to analyze and predict the trend of the prices of gold.

Mathematical modeling for finantial time series data

Source code for predicting Blood Glucose Concentration

A collection of notebooks and different prediction models that can predict the stock prices. Also a comparison of how all these models performed.

LSTM for time series forecasting

PhD Thesis: "Data Science in the Modeling and Forecasting of Financial Timeseries: from Classic methodologies to Deep Learning"

Projects of Business Analyst Nanodegree Program

In this project two models are build a Multivariate CNN-LSTM model using keras and tensorflow, ARIMA model, and FbProphet. In multivariate CNN-LSTM five feature are given as a input to the model and output as Closing price. Forecasted for the next 30 days. the dataset has been collected from Yahoo finance.

Forecasting Monthly Sales of French Champagne - Perrin Freres

Deep Reinforcement Learning for Trading

My Solution to the Assignment Task based on Telecom Italia's Big Data Challenge