LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns

09/10/2019
by   Kasun Bandara, et al.
17

Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these contexts. In this paper, we propose Long Short-Term Memory Multi-Seasonal Net (LSTM-MSNet), a decompositionbased, unified prediction framework to forecast time series with multiple seasonal patterns. The current state of the art in this space are typically univariate methods, in which the model parameters of each time series are estimated independently. Consequently, these models are unable to include key patterns and structures that may be shared by a collection of time series. In contrast, LSTM-MSNet is a globally trained Long Short-Term Memory network (LSTM), where a single prediction model is built across all the available time series to exploit the crossseries knowledge in a group of related time series. Furthermore, our methodology combines a series of state-of-the-art multiseasonal decomposition techniques to supplement the LSTM learning procedure. In our experiments, we are able to show that on datasets from disparate data sources, like e.g. the popular M4 forecasting competition, a decomposition step is beneficial, whereas in the common real-world situation of homogeneous series from a single application, exogenous seasonal variables or no seasonal preprocessing at all are better choices. All options are readily included in the framework and allow us to achieve competitive results for both cases, outperforming many state-ofthe-art multi-seasonal forecasting methods

READ FULL TEXT

page 1

page 2

page 6

page 8

research
01/13/2019

Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology

Generating accurate and reliable sales forecasts is crucial in the E-com...
research
10/09/2017

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series

With the advent of Big Data, nowadays in many applications databases con...
research
03/11/2023

A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series

A univariate time series with high variability can pose a challenge even...
research
09/20/2022

An Attention Free Long Short-Term Memory for Time Series Forecasting

Deep learning is playing an increasingly important role in time series a...
research
05/31/2019

A multi-series framework for demand forecasts in E-commerce

Sales forecasts are crucial for the E-commerce business. State-of-the-ar...
research
11/28/2017

Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings

Knowing how many people occupy a building, and where they are located, i...

Please sign up or login with your details

Forgot password? Click here to reset