holt-winters-forecasting

There are 59 repositories under holt-winters-forecasting topic.

  • Forecasting-in-R

    Language:R
  • SmoothTrend

    SmoothTrend

    SmoothTrend is a comprehensive time series analysis tool that utilizes Holt-Winters, Holt, and Simple Exponential Smoothing methods, as well as ARIMA/SARIMA modeling, to perform advanced trend analysis, stationarity testing, residual analysis, and forecasting.

    Language:Python

  • holt_winters_exp_smooth_tf

    Partial port of statsmodels Holt Winters exponential smoothing library using tensorflow for matrix operations and optimization

    Language:JavaScript

  • Analyzing-Amazon-s-Revenue-Exploring-Forecasting-Models

    This repository presents a comprehensive analysis of Amazon's revenue forecasting for the fiscal year 2021, analyzing historical revenue data from 2010 to 2020 and identifying the most suitable forecasting model that accurately predicts Amazon's revenue trends considering trend and seasonality.

  • Airline_Passenger_Prediction

    Airline passenger traffic prediction using time series forecasting techniques

    Language:Jupyter Notebook

  • Procter-Gamble-s-stock-prices-forecasting

    The objective is to forecast Procter & Gamble's stock performance using time series analysis to provide valuable insights for investors and stakeholders.

    Language:R

  • Daily-Climate-Time-Series

    Times Series Analysis of Daily Climate dataset using traditional methods

    Language:Jupyter Notebook

  • Forecast-Export-Minyak_dan_Gas

    Holt-Winters method to forecast time series data, evaluating accuracy, and Visualized predictions. Enhanced data-driven decision-making.

  • ForecastingwithHoltWinterMethod

    Forecasting time series data using Holt Winter's Model in Python

    Language:Jupyter Notebook

  • TS-ICMS-Previsao

    previsão: Suavização Exponencial, Holt-Winters, SARIMA

    Language:Jupyter Notebook

  • Forecasting-and-Classification-Data-Factory

    In first project: revenue prediction for two product categories with the best results on the first income using the ETNA library and in the second income using the Holt Winters method. In Second project: clients classification on potentially ready for churn and not.

    Language:Jupyter Notebook

  • City-and-Resort-Hotels-Bookings-Forecast

    Time Series Forecasting: City and Resort Hotels Bookings forecasting

    Language:Jupyter Notebook

  • TESLA-stock-price-forecasting

    Time series forecasting techniques to predict TESLA's stock price

    Language:Jupyter Notebook

  • Time-Series-Forecast-Medium-Daily-Posts-Forecast

    Time Series Forecasting Methods to forecast Daily Post Publications on Medium

    Language:Jupyter Notebook

  • Beer_Sales_Forecasting_Spanish_Market

    Implemented the Holt-Winters Triple Exponential Smoothing available in the stats model package in Python to make sales projections of total sales and sales per product segment for client in the Spanish beer market

    Language:Jupyter Notebook

  • SFO-Passengers_TimeSeriesAnalysis

    Time Series Analysis and Forecasting with R

    Language:Jupyter Notebook

  • Umass-Senior-Project-2019

    The purpose of my application was to solve a problem many businesses (small businesses in particular) face. They do not know how much to produce, where to price, how much to spend on advertising and many other questions. Eden’s purpose was to answer these questions for them easily and with no technical acumen required by the user. Eden would model supply and demand equations using ordinary least squares (OLS) regression on the user’s data to form the best fitting supply and demand equations possible. The best fit was to be ensured by regressing each variable against demand or supply, determine the best shape via the highest adjusted R2, and then do an OLS regression and simplistically tell the user what the results mean. Eden would attempt multiple shapes like linear, logarithmic, cubic, quadratic, and inverse. The user interface would be easy to navigate and user-friendly.

  • Fit-data-with-Increasing-trend-and-varying-seasonal-component

    Fit and predict on a data with an increasing trend and a varying seasonal component.

    Language:R

  • HoltWinters

    Basic Holt Winter Covid-19 Example

    Language:Jupyter Notebook

  • RetailSales

    [Times series analysis] Retail Sales in the USA (1999 - 2020): The rise of E-commerce

    Language:Jupyter Notebook

  • nanditobandito.github.io

    Classifying and Forecasting Student Performance at ITCH

    Language:Jupyter Notebook

  • HoltWinters

    Holt-Winters Timeseries Forecast

    Language:Python

  • COVID-19

    This repository is B.Tech. major project on COVID-19 Global and India Forecast

    Language:Jupyter Notebook