Statistical toolkit to make time-series stationary
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Updated
Jul 11, 2024 - Python
Statistical toolkit to make time-series stationary
🌾 R package to provide an efficient and simple solution to aggregate and analyze the stationarity of time series
🥤 MAKAHO (for MAnn-Kendall Analysis of Hydrological Observations) is an interactive cartographic visualization system that allows to calculate trends present in data from hydrometric stations with flows which are little influenced by human actions
Forecasting Wine Sales of Two Different types of Wine. After thorough Data Analysis, different models have been used and tested such as Exponential Smoothing Models, Regression, Naive Forecast, Simple Average, Moving Average. Stationarity of the data is checked. Automated Version of ARIMA/SARIMA Model built. Comparison of Models.
🛠️ R toolbox to provide a simple way of interacting with all the code necessary to carry out hydrological stationnarity analysis for the Agence de l'Eau Adour-Garonne (AEAG)
Unit root tests in Rust
Used First Difference Method for Stationarity of the Time Series and then Used ARIMA & SARIMA to predict the values and based on the prediction, checked if the series contains Seasonal Patterns in it or not
Forecast the Airlines Passengers. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.
Time Series forecasting using Seasonal ARIMA. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots. Transformed series to make it stationary
This repository analyzes the Multivariate workload data of Google Cluster machines.
Automating time series stationarity tests
Code for analyzing statistical features of several precipitation stations
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