# Forecasting with Deep Learning

## TL;DR

This paper explores deep learning for time series forecasting, showing it works well with patterned data but struggles with less structured series like stock prices, and provides open-source implementation.

## Contribution

It introduces a deep learning-based forecasting method and evaluates its effectiveness across different types of time series datasets.

## Key findings

- Deep learning models perform well on patterned time series.
- Models struggle with unstructured data like stock prices.
- Open-source implementation is provided.

## Abstract

This paper presents a method for time series forecasting with deep learning and its assessment on two datasets. The method starts with data preparation, followed by model training and evaluation. The final step is a visual inspection. Experimental work demonstrates that a single time series can be used to train deep learning networks if time series in a dataset contain patterns that repeat even with a certain variation. However, for less structured time series such as stock market closing prices, the networks perform just like a baseline that repeats the last observed value. The implementation of the method as well as the experiments are open-source.