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+# global-forecasting
+Paper on global and local methods for time series forecasting.
+Intended for reproducibility of the results in the paper
+TITLE, not for actual forecasting (though it can be used as such, it is not supported).
+
+Everything in the repo is based on R (code, datasets) and Latex.
+
+
+
+## Contents
+
+ * Installation instructions
+ * The code for global models
+ * Experiments
+ * Analysis of Results
+ * Data
+ * Paper source
+
+## Installation instructions
+
+Because the data is quite large, besides checking the repo
+the file: LINK should be downloaded and extracted in the src folder.
+So you should have src/data and src/results folders after extracting.
+
+
+
+
+## Basic Global Models
+
+Modeling functions are in src/model.R
+
+The main convenience function is `analyze_glob_model`
+That takes `my_series` a list of series with the elements in a format
+similar to the Mcomp, with elements:
+
+ * $x The observed time series
+ * $xx The future part
+ * $id
+ * $feat
+
+Another parameter is `fit_model_fun`, the modeling function, it can be
+`linear_model`, `poly_2_model`, `poly_3_model`, `tree_model`, `deep_model`.
+
+Another parameter is `MASE_norm`, whether to apply normalization by the MASE denominator
+to each series. It is `TRUE` by default, this normalzation was applied to most experiments in the paper.
+
+Another parameter is `lag_range`, the lags orders to experiment with, for example 
+`lag_range=20:50` will compute the forecasts for all global models from lag 20 to lag 50.
+
+The parameter `model_name` just identifies the model that is being used with the given string, attaching the lag order,
+in the output forecasts.
+
+The output of `analyze_glob_model` are the average errors calculated for the dataset: MAE, SMAPE.
+
+## Experiments
+
+The file src/experiments.R has the code to launch the experiments.
+Just set the working directory, load model.R and run the experiments (it will take a lot of time).
+You might take the experiments.R file as inspiration to run your own tests.
+
+To simplify, we have added the results of the experiments in the results folder.
+
+Some experiments might be missing (e.g. a few datasets) in an almost two year
+development cycle. How to run them again should be self-explanatory given the data.
+
+## Analyzing the experiments
+
+The code to 
+
+
+Analyze
+
+## Datasets
+The folder src/data contains the datasets that were used in the experiments.
+The filer src/pre_datasets.R has the code to process the datasets from their raw values
+to version it was finally used (data sources, data cleaning and so on).
+