GitHub / Honey28Git / Time-Series-Forecasting
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.
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PURL: pkg:github/Honey28Git/Time-Series-Forecasting
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Language: Jupyter Notebook
Size: 4.3 MB
Dependencies parsed at: Pending
Created at: over 1 year ago
Updated at: over 1 year ago
Pushed at: over 1 year ago
Last synced at: over 1 year ago
Topics: acf-pacf, arima-model, decomposition, exponential-smoothing-models, moving-average, naive-forecasting, prediction, regression-models, rmse-score, sarimax, simple-average, stationarity-test, time-series-analysis, wine-quality