GitHub - SayamAlt/Time-Series-Analysis-Forecasting: This repository covers essential techniques for time series analysis and forecasting. It covers data manipulation and visualization using Numpy and Pandas, time series analysis with Statsmodels, ARIMA models, deep learning methods like RNNs, LSTM, GRU, etc. and Facebook's Prophet library.

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This repository covers essential techniques for time series analysis and forecasting. It covers data manipulation and visualization using Numpy and Pandas, time series analysis with Statsmodels, ARIMA models, deep learning methods like RNNs, LSTM, GRU, etc. and Facebook's Prophet library.

arima-forecasting augmented-dickey-fuller-test autocorrelation-function deep-learning facebook-prophet-forecasting forecasting-models holt-winters-forecasting partial-autocorrelation-function python recurrent-neural-networks sarima-model sarimax seasonality stationarity-test statsmodels time-series-analysis time-series-forecasting trends varmax

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Name		Name	Last commit message	Last commit date
Data		Data
02-Pandas-Data-Visualization-Exercise.ipynb		02-Pandas-Data-Visualization-Exercise.ipynb
02-RNN-Exercise.ipynb		02-RNN-Exercise.ipynb
04-BONUS-Multivariate-RNN.ipynb		04-BONUS-Multivariate-RNN.ipynb
04-Statsmodels-Time-Series-Exercises.ipynb		04-Statsmodels-Time-Series-Exercises.ipynb
05-Pandas-Time-Series-Exercises-SET-ONE.ipynb		05-Pandas-Time-Series-Exercises-SET-ONE.ipynb
07-Time-Series-with-Pandas-Project-Exercise-SET-TWO.ipynb		07-Time-Series-with-Pandas-Project-Exercise-SET-TWO.ipynb
10-Forecasting-Exercises.ipynb		10-Forecasting-Exercises.ipynb
ACF and PACF.ipynb		ACF and PACF.ipynb
ARMA and ARIMA modeling.ipynb		ARMA and ARIMA modeling.ipynb
Alcohol Sales Forecasting using LSTMs.ipynb		Alcohol Sales Forecasting using LSTMs.ipynb
Appliances Energy Prediction using LSTMs(Multivariate Time Series).ipynb		Appliances Energy Prediction using LSTMs(Multivariate Time Series).ipynb
Autoregression (AR) with Statsmodels.ipynb		Autoregression (AR) with Statsmodels.ipynb
Choosing ARIMA Orders.ipynb		Choosing ARIMA Orders.ipynb
Customizing plots with Pandas.ipynb		Customizing plots with Pandas.ipynb
Dealing with Datetime Index objects.ipynb		Dealing with Datetime Index objects.ipynb
Descriptive Statistics and Tests.ipynb		Descriptive Statistics and Tests.ipynb
ETS Decomposition.ipynb		ETS Decomposition.ipynb
Exponentially Weighted Moving Averages (EWMA).ipynb		Exponentially Weighted Moving Averages (EWMA).ipynb
Facebook Prophet Evaluation.ipynb		Facebook Prophet Evaluation.ipynb
Facebook Prophet Seasonality.ipynb		Facebook Prophet Seasonality.ipynb
Facebook Prophet Trend.ipynb		Facebook Prophet Trend.ipynb
Facebook's Prophet Time Series Forecasting.ipynb		Facebook's Prophet Time Series Forecasting.ipynb
Forecasting at Scale.pdf		Forecasting at Scale.pdf
Introduction to Forecasting Models.ipynb		Introduction to Forecasting Models.ipynb
Keras Basics.ipynb		Keras Basics.ipynb
Rolling and Expanding.ipynb		Rolling and Expanding.ipynb
SARIMAX - Seasonal Autoregressive Integrated Moving Average Modeling.ipynb		SARIMAX - Seasonal Autoregressive Integrated Moving Average Modeling.ipynb
Seasonal Autoregressive Integrated Moving Average - SARIMAX Modeling.ipynb		Seasonal Autoregressive Integrated Moving Average - SARIMAX Modeling.ipynb
Statsmodels Introduction.ipynb		Statsmodels Introduction.ipynb
Time Resampling.ipynb		Time Resampling.ipynb
Time Series Analysis using Holt-Winters Method.ipynb		Time Series Analysis using Holt-Winters Method.ipynb
Time Shifting.ipynb		Time Shifting.ipynb
Vector AutoRegression(VAR) Modeling.ipynb		Vector AutoRegression(VAR) Modeling.ipynb
Vector AutoRegressive Moving Average (VARMA) Modeling.ipynb		Vector AutoRegressive Moving Average (VARMA) Modeling.ipynb
Visualizing time series data with Pandas.ipynb		Visualizing time series data with Pandas.ipynb
Visualizing time series data.ipynb		Visualizing time series data.ipynb
alcohol_sales_forecaster.keras		alcohol_sales_forecaster.keras
appliances_energy_predictor.h5		appliances_energy_predictor.h5
tsa_course_env.yml		tsa_course_env.yml
vehicle_miles_travelled_forecaster.h5		vehicle_miles_travelled_forecaster.h5

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