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Honey28Git / Time-Series-Forecasting

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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.

prediction decomposition regression-models time-series-analysis wine-quality sarimax moving-average arima-model exponential-smoothing-models acf-pacf rmse-score stationarity-test simple-average naive-forecasting
  • Updated Mar 27, 2024
  • Jupyter Notebook

razamehar / Weather-Time-Series-Analysis-using-Statistical-Methods-and-Deep-Learning-Models

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This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.

neural-network python3 lstm gru smoothing weather-forecast bidirectional-lstm time-series-analysis moving-average differencing seasonality qqplots learning-rate-scheduling naive-forecasting centered-approach
  • Updated May 25, 2024
  • Jupyter Notebook

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