Introduction

Time series forecasting is a crucial aspect of various domains like finance, supply chain management, weather prediction, and more. The objective of time series forecasting is to predict future points by learning patterns from historical data. In recent years, Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have gained prominence in addressing time series problems due to their ability to capture temporal dependencies.

In this article, we delve into how to handle multi-step time series forecasting with multivariate data using LSTM networks in Keras. We explore concepts, methods, and technical implementation steps, enriched with examples for better comprehension.

Multivariate Time Series Forecasting

Definition

A multivariate time series contains multiple variables that are recorded over time. All these variables might interact with each other and contribute to the future values we aim to predict. For instance, predicting the weather involves multiple parameters like temperature, humidity, wind speed, etc.

Challenges

1. Complexity: The interdependencies among multiple variables make multivariate forecasting more challenging than univariate.
2. Data Preparation: Converting these data points into a format suitable for LSTM models can be intricate.

Steps in Multi-Step Forecasting

1. Data Preprocessing
2. Creating Sequences
3. Building the LSTM Model
4. Training the Model
5. Evaluating and Making Predictions

Technical Implementation with Keras

We'll use Keras, a high-level neural networks API, to build our LSTM model. Keras simplifies the process of building and training deep learning models.

1. Data Preprocessing

Before feeding the data into an LSTM model, it should be properly prepared:

• Normalize the Data: This scales the features to lie between 0 and 1, often necessary for neural networks to converge efficiently.
• Splitting Data: Divide the dataset into train and test sets.