Small computers, such as Arduino devices, can be used within buildings to record environmental variables from which simple and useful properties can be predicted. One example is predicting whether a room or rooms are occupied based on environmental measures such as temperature, humidity, and related measures. This is a type of common time series classification […]

# Archive | Deep Learning for Time Series

## Predict Whether a Persons Eyes are Open or Closed Using Brain Waves

A Case Study in How to Avoid Methodological Errors when Evaluating Machine Learning Methods for Time Series Forecasting. Evaluating machine learning models on time series forecasting problems is challenging. It is easy to make a small error in the framing of a problem or in the evaluation of models that give impressive results but result […]

## 4 Common Machine Learning Data Transforms for Time Series Forecasting

Time series data often requires some preparation prior to being modeled with machine learning algorithms. For example, differencing operations can be used to remove trend and seasonal structure from the sequence in order to simplify the prediction problem. Some algorithms, such as neural networks, prefer data to be standardized and/or normalized prior to modeling. Any […]

## How to Develop a Skillful Machine Learning Time Series Forecasting Model

You are handed data and told to develop a forecast model. What do you do? This is a common situation; far more common than most people think. Perhaps you are sent a CSV file. Perhaps you are given access to a database. Perhaps you are starting a competition. The problem can be reasonably well defined: […]

## Taxonomy of Time Series Forecasting Problems

When you are presented with a new time series forecasting problem, there are many things to consider. The choice that you make directly impacts each step of the project from the design of a test harness to evaluate forecast models to the fundamental difficulty of the forecast problem that you are working on. It is […]

## How to Prepare Univariate Time Series Data for Long Short-Term Memory Networks

It can be hard to prepare data when you’re just getting started with deep learning. Long Short-Term Memory, or LSTM, recurrent neural networks expect three-dimensional input in the Keras Python deep learning library. If you have a long sequence of thousands of observations in your time series data, you must split your time series into […]

## Multivariate Time Series Forecasting with LSTMs in Keras

Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can […]

## How to Get Good Results Fast with Deep Learning for Time Series Forecasting

3 Strategies to Design Experiments and Manage Complexity on Your Predictive Modeling Problem. It is difficult to get started on a new time series forecasting project. Given years of data, it can take days or weeks to fit a deep learning model. How do you get started exactly? For some practitioners, this can lead to […]

## How to Remove Trends and Seasonality with a Difference Transform in Python

Time series datasets may contain trends and seasonality, which may need to be removed prior to modeling. Trends can result in a varying mean over time, whereas seasonality can result in a changing variance over time, both which define a time series as being non-stationary. Stationary datasets are those that have a stable mean and […]

## How to Scale Data for Long Short-Term Memory Networks in Python

The data for your sequence prediction problem probably needs to be scaled when training a neural network, such as a Long Short-Term Memory recurrent neural network. When a network is fit on unscaled data that has a range of values (e.g. quantities in the 10s to 100s) it is possible for large inputs to slow […]