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

Source code for predicting Blood Glucose Concentration

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

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

Projects of Business Analyst Nanodegree Program

Mathematical modeling for finantial time series data

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

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

A hybrid forecasting model combining LSTM for sequence prediction and ARIMA for error correction. This repo demonstrates improved accuracy in financial trend prediction, showcasing training processes, error analysis, and performance metrics.

Deep Reinforcement Learning for Trading

LSTM for time series forecasting

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.

Companion and Download Site for the SAS Press Book "Applying Data Science - Business Case Studies Using SAS"

Forecasting Monthly Sales of French Champagne - Perrin Freres

A statistical decomposition of internet traffic data (in bits) over time. Using RStudio I performed a Simple Trend Model, Multiplicative Classical Decomposition, Additive Classical Decomposition, and an ARIMA model.

ARIMA model from scratch using numpy and pandas.

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

Bitcoin price prediction using ARIMA Model.

Explore TESLA stock price (time-series) using ARIMA & GARCH model.

A time-series companion package to healthyR

Time Series Analysis for Oil Price Prediction

An overview of various quantitative techniques and trading strategies for predicting stock prices, based on historical data from YahooFinance.

使用经典的AR、MA、ARMA、ARIMA、ARCH、GARCH时间序列模型进行模型的检验和拟合。The classic AR, MA, ARMA, ARIMA, ARCH, GARCH time series models are used to test and predict the model.