Can a Long Short-Term Memory Model Produce Accurate Stock Price Predictions?: A Deep Learning Approach to Predicting Apple Inc. Stock Price.

[R] Statistical analysis of financial data conducted in R

Pharma Sales Analysis and Forecasting using ARIMA, PROPHET and NEURAL NETWORKS

This repository contains source code implementation of assignments for NTU's MSAI course AI6123 on Time Series Analysis (2019 Sem 2).

Predictive analysis and GARCH model on stock returns. I demonstrate how to use the PACF (partial autocorrelation function) and ACF (autocorrelation function) on a non stationary time series.

Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step.

Trabalho realizado para aprovação na disciplina de Análise de Séries Temporais. Foi realizado a análise e modelagem da serie temporal da entrega de fertilizantes ao mercado brasileiro em mil toneladas no período mensal de janeiro de 1998 até abril de 2020 (Fonte: ANDA)

Predict the apple stock market price for next 30 days. There are Open, High, Low and Close price has been given for each day starting from 2012 to 2019 for Apple stock.

ACF || PACF || ARIMA || SARIMA

Detailed implementation of various time series analysis models and concepts on real datasets.

Project work for Time Series Analysis. Includes exploratory analysis, ARIMA modeling, diagnostics, forecasting, and evaluation using R. Covers trend/seasonality modeling, stationarity checks, ACF/PACF analysis, model selection, and forecast accuracy assessment.

Airplane passenger forecast using SARIMA Model Acc: (96.63%)

In this notebook, I've loaded historical Dollar-Yen exchange rate futures data. I've applied time series analysis and modeling to determine whether there is any predictable behavior.

No description provided.

Auto ARIMA Model

Cloud-based Ethereum (ETH/USDT) price forecasting app using ARIMA time series modeling. Built with Streamlit, Python, and free public APIs for fully online deployment and zero local storage.

Time Series Forecasting Methods to forecast Daily Post Publications on Medium

No description provided.

Time-series demand forecasting using SARIMA. Includes full pipeline for stationarity analysis, ACF/PACF diagnostics, SARIMA model training, forecasting, and performance evaluation.

This model predicts furniture sales on account of 4 year sales record.

Prediction of prices of selected cryptocurrencies using the ARIMA model.

Financial Time Series Analysis

A general understanding of Statistics Basics, Different tests with Different Python Libraries

No description provided.

total raw governmental industry employment data from January 1 1939 to October 30 2019. Time Series analysis to forecast employment from October 2019-October 2020.

This project uses time series forecasting to predict future milk production. The data used in this project is monthly milk production data from January 1962 to December 1975. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The model is evaluated using various metric.

Used time series model to forecast

A practical ARMA modeling implementation in Python. Explores theory, data analysis, model fitting, diagnostics, forecasting, and advanced extensions, utilizing statsmodels, pandas, and matplotlib.

This project uses time series forecasting to predict future milk production. The data used in this project is monthly milk production data from January 1962 to December 1975. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The model is evaluated using various metric.