Correlation Mining between Time Series Stream and Event Stream

Minaei-Bidgoli, Behrouz; Lajevardi, Saina

doi:10.1109/ncm.2008.223

Cited by 11 publications

(1 citation statement)

References 7 publications

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“…Forecasting of future samples plays a vital role in guiding the decision making of selected areas. Data that is required for time series forecasting are classified as two types: one is time series data and another one is data with time points [2]. Time series data can be described as…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of the outcomes of differing time series forecasting strategies

Masum

Liu

Chiverton

2017

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

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Time series forecasting of data from various domains has become an increasingly interesting research subject in recent times. Prediction of future sample values is the main goal of time series forecasting. There are mainly two classes of time series forecasting, namely, single step and multi-step forecasting. There are various machine learning approaches that can be used along with various forecasting strategies which exist, some of which can be utilized in and by time series forecasting. However, inappropriate use of strategies can limit the applicability of the techniques to real world problems. This paper provides a review of time series forecasting, a classification of time series forecasting and also approaches and strategies of time series forecasting. It also demonstrates how inappropriate use of point to point rolling forecast strategy for forecasting could lead to unrealistic outcomes and how a multiple step forecasting strategy called Direct H step strategy could help to overcome this issue. Comparative analysis of two strategies using Auto-Regressive Integrated Moving Average (ARIMA) approach are demonstrated.

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Section: Introductionmentioning

confidence: 99%

Comparative analysis of the outcomes of differing time series forecasting strategies

Masum

Liu

Chiverton

2017

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

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Multi-step Time Series Forecasting of Electric Load Using Machine Learning Models

Masum

Liu

Chiverton

2018

Lecture Notes in Computer Science

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Multi-step forecasting is very challenging and there are a lack of studies available that consist of machine learning algorithms and methodologies for multi-step forecasting. It has also been found that lack of collaborations between these different fields is creating a barrier to further developments. In this paper, multi-step time series forecasting are performed on three nonlinear electric load datasets extracted from Open-Power-System-Data.org using two machine learning models. Multi-step forecasting performance of Auto-Regressive Integrated Moving Average (ARIMA) and Long-Short-Term-Memory (LSTM) based Recurrent Neural Networks (RNN) models are compared. Comparative analysis of forecasting performance of the two models reveals that the LSTM model has superior performance in comparison to the ARIMA model for multi-step electric load forecasting.

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