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

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

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。

LSTM for time series forecasting

Mathematical modeling for finantial time series data

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

Jupyter Notebooks Collection for Learning Time Series Models

Time Series Analysis for Oil Price Prediction

Building Event Extraction and Trending Framework for Twitter

Forecasting Monthly Sales of French Champagne - Perrin Freres

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

Predication of stock market price using different machine learning models

Time Series Analysis Concepts Explained with examples

Analyze NASDAQ100 stock data. Used ARIMA + GARCH model and machine learning techniques Naive Bayes and Decision tree to determine if we go long or short for a given stock on a particular day

Here we are basically doing Time Series Forecasting of May month by using ARIMA Model.

In this project, this research generally investigates the financial time series such as the price & return of NASDAQ Composite Index using ARIMA and GARCH methods.

It is well known that the stock market exhibits very high dimensionality due to the almost unlimited number of factors that can affect it which makes it very difficult to predict. Studying how global stock market indexes respond to headlines can provide a major advantage in predicting stock movements and making trade decisions. Naturally, fundamental and technical indicators are not to be neglected and the goal of the project is to combine all of these aspects to achieve a model that thinks as an experienced trader.

Predictive Analytics

Smart India Hackathon 2019

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

Forecasting using Linear Regression, Smoothing techniques, ARIMA Models, SARIMA Models and other data driven and model based techniques.

Sales Forecast of Automobile Cars using R programming

R package consisting of functions and tools to facilitate the use of traditional time series and machine learning models to generate forecasts on univariate or multvariate data. Different backtesting scenarios are available to identify the best performing models.

Machine learning model built for IBM Hack 2020 challenge. ⚙️

Projects of Business Analyst Nanodegree Program