Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

A Univariate Time Series Analysis and ARIMA Modeling Package in ANSI C. Updated with SARIMAX and Auto ARIMA.

I perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The Autoregressive Moving Average (ARMA) Model, The Autoregressive Integrated Moving Average (ARIMA) Model, The ARCH Model, The GARCH model, Auto ARIMA, forecasting and exploring a business case.

Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest Neighbor, Support Vector Machine, Decision Tree, and Random Forest to identify which algorithm gives better results. Used Neural Networks such as Auto ARIMA, Prophet(Time-Series), and LSTM(Long Term-Short Memory…

Temperature and Dewpoint Forecasting using the ARIMA and Auto-ARIMA model respectively.

Automatic multi-seasonal ARIMA Learning

This a Capstone Project done by Team Pycaret in Hamoye Data Science Program Fall'22. ARIMA and Prophet model were used to forecast the closing price of currency exchange. An app was deployed with a friendly UI where users can easily make forecast on a currency pair of their choice, based on the available data used.

This repo contains my time series forecasting project to forecast international airline passengers.

Novel-Corona Virus or Covid-19 :Visualisation,Forecasting ,Analysis,Maps,Bar Race Charts,Starter Codes,Modelling,Forecasting,Estimation

This repo uses Natural Langauage Processing, time series analysis, and ARIMA to explore predictive housing trend analysis.

This is a group project for MTH416A: Regression Analysis at IIT Kanpur

In this 11 project series, we will explore Data Science concepts using different Kaggle datasets.

View my industry practicum for predicting amazon profitability and trade optimization using SKU bundling strategy - for a New York based Fortune 200 CPG company's ECommerce Analytics and Insights team

This study compares popular Machine Learning (ML), Deep Learning (DP), and statistical algorithms for forecasting microservice time series.

Auto-ARIMA timeseries forecasting in combination with PELT changepoint detection to predict social media viewership performance and identify major changes in performance trends. Models are deployed into a streamlit webapp for analytical functionality.

Enhanced Automatic time series forecasting using ARIMA family models

Using this jet rails real life problem, i tried explaining all the time series algorithms to get better understanding of time series.

Examining different cases with time series analysis and making predictions or forecasting.

This project comprises of automating creating an LSTM as well choosing certain tickers from Yahoo! Finance into a Streamlit app to display results for Auto ARIMA and LSTM models for making predictions of cryptocurrency values.