An easy to use low-code open-source python framework for Time Series analysis, visualization, forecasting along with AutoTS
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Updated
Jan 6, 2022 - Python
An easy to use low-code open-source python framework for Time Series analysis, visualization, forecasting along with AutoTS
Python library to forecast univariate time series through backtesting model selection
Project In Ecole Centrale Casablanca
Forecasted traffic volume from 2016-2018 data to reduce traffic congestion and efficiently schedule road maintenance using time series models.
Project In Ecole Centrale Casablanca
Web App Forecasting Top 5 Crypto Use Triple Exponential Smoothing and ARIMA
Keras, Tensorflow eager execution layers for exponential smoothing
Automated the process of training time-series data with multiple Machine Learning and Stats Models to output the most accurate forecast result
The purpose of this project is to demonstrate the application of three main forecasting functions: single exponential smoothing, double exponential smoothing and Holt-Winters forecasting.
e-Portfolio showcasing my personal projects.
Holt-Winters Timeseries Forecast
Implementation of ARIMA and Holt-Winters Exponential Smoothing models for the analysis of the global network of human vaccines for the period 2010-2019
Implemented and compared the performances of various Algorithms like ARIMA, VAR, and Holt-Winters to predict the weather in a district in India. Normalized various numerical parameters like sunshine, minimum temperature, maximum temperature, humidity etc.
Data smoothing visualization
I implement a ETS (ANN) and ETS (AAA) model, followed by a Simple Exponential Smoothing, Holt and Holt-Winters model. In conclusion I compare the results and recommend the best alternative.
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