Build models for forecasting Airline passenger traffic by utilizing several algorithms for time series analysis.

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.

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.

Splitting data, Moving Average, Time series decomposition plot, ACF plots and PACF plots, Evaluation Metric MAPE, Simple Exponential Method, Holt method, Holts winter exponential smoothing with additive seasonality and additive trend, Holts winter exponential smoothing with multiplicative seasonality and additive trend, Final Model by combining …

Need to predict how many passengers are going to opt for the airline base on the historical information provided by the Airlines. Using various Time series techniques predicted the number of passengers

In this section, there is a discussion of the Exponential Smoothing Method in time series analysis.

Airline passenger traffic prediction using time series forecasting techniques

The data of different types of wine sales in the 20th century is to be analysed. Both of these data are from the same company but of different wines. As an analyst in the ABC Estate Wines, you are tasked to analyse and forecast Wine Sales in the 20th century.

Real-time imputation of missing environmental sensor data for fault-tolerant edge computing.

2021 Amirkabir Artificial Intelligence Competitions (AAIC): Challenge of forecasting daily internet usage of MCI subscribers

Time Series Forecasting Methods to forecast Daily Post Publications on Medium

Industrial Production Index Time Series Forecasting using a range of models including Holt-Winters, ARIMA, SARIMA, LSTMs, and Facebook's Prophet. The project focuses on predicting production trends through model evaluation, tuning, and visualization of forecasted outcomes.