This repository contains a few forecasting projects written in R. Besides the general visualization and decompositions, several forecasting techniques have been applied:
- statistical methods, such as exponential smoothing, Holt, linear regression and theta
- Machine Learning methods, such as Deep Learning (Neural Network) and decision trees (XGBoost)
- Data visualization
- Filling missing values
- Identifying outliers + normalization
- Visualize weekly and monthly series
- Decomposition of trend and seasonal components
- Data visualization
- Some measures for forecastability are computed: ADI and CV^2
- Empirical distribution of the demand of the product and compute some percentiles of number of daily sales
- Decomposition of seasonal, trend and random components
- Decomposition in seasonal and trends components
- Generate forecasts using Simple, Holt and Damped Exponential Smoothing
- Measuring accuracy including prediction intervals
- Plotting some data for better understanding
- Forecasting of power generation by applying a Linear Regression Model
- Forecasting of power generation by applying a Neural Network
- Simple tests for missing values and outlier detection
- Normalizing and imputing these
- Correlation matrix
- Decomposition
- Statistical forecasting: ** insample/outsample data separation ** applying theta forecasting
- ML forecasting: ** scaling of data ** factorizing ** including data lag (1 week, 2 weeks) ** insample/outsample ** define NN model and hyperparameters ** define XGBoost model and hyperparameters
- Compare scores of all models