Time series prediction and forecasting using Deep learning Architectures

Nature brings time series data everyday and everywhere, for example, weather data, physiological signals and biomedical signals, financial and business recordings. Predicting the future observations of a collected sequence of historical observations is called time series forecasting. Forecasts are essential, considering the fact that they guide decisions in many areas of scientific, industrial and economic activity such as in meteorology, telecommunication, finance, sales and stock exchange rates. A massive amount of research has already been carried out by researchers over many years for the development of models to improve the time series forecasting accuracy. The major aim of time series modelling is to scrupulously examine the past observation of time series and to develop an appropriate model which elucidate the inherent behaviour and pattern existing in time series. The behaviour and pattern related to various time series may possess different conventions and infact requires specific countermeasures for modelling. Consequently, retaining the neural networks to predict a set of time series of mysterious domain remains particularly challenging. Time series forecasting remains an arduous problem despite the fact that there is substantial improvement in machine learning approaches. This usually happens due to some factors like, different time series may have different flattering behaviour. In real world time series data, the discriminative patterns residing in the time series are often distorted by random noise and affected by high-frequency perturbations. The major aim of this thesis is to contribute to the study and expansion of time series prediction and multistep ahead forecasting method based on deep learning algorithms. Time series forecasting using deep learning models is still in infancy as compared to other research areas for time series forecasting.Variety of time series data has been considered in this research. We explored several deep learning architectures on the sequential data, such as Deep Belief Networks (DBNs), Stacked AutoEncoders (SAEs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Moreover, we also proposed two different new methods based on muli-step ahead forecasting for time series data. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis makes theoretical, methodological and empirical contributions to time series prediction and multi-step ahead forecasting by using Deep Learning Architectures

Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models

We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend and seasonal. Periodic time series models allow dynamic characteristics to depend on the period of the year, month, week or day. In the standard multivariate approach one can interpret periodic time series modelling as a simultaneous analysis of a set of, traditionally, yearly time series where each series is related to a particular season, with a time index in years. Our analysis applies to monthly vector time series related to each day of the month. We focus on forecasting performance and the underlying periodic forecast function, defined by the in-sample observation weights for producing (multi-step) forecasts. These weights facilitate the interpretation of periodic model extensions. We take a statistical state space approach to estimate our model, so that we can identify stochastic unobserved components and we can deal with irregularly spaced time series. We extend existing algorithms to compute observation weights for forecasting based on state space models with regressor variables. Our methods are illustrated by an application to time series of clearly periodic daily Dutch tax revenues. The dimension of our model is large as we allow the time series for each day of the month to be subject to a changing seasonal pattern. Nevertheless, even with only five years of data we find that increased periodic flexibility helps help in simulated out-of-sample forecasting for two extra years of data

Financial Time Series Forecasts using Fuzzy and Long Memory Pattern Recognition Systems

Copyright © 2000 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering, 2000 (CIFEr), New York, USA, 26 - 28 March 2000In this paper, the concept of long memory systems for forecasting is developed. The pattern modelling and recognition system and fuzzy single nearest neighbour methods are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, the PMRS system has been successfully used for forecasting the Santa Fe competition data. In this paper, we forecast the FTSE 100 and 250 financial returns indices, as well as the stock returns of five FTSE 100 companies and compare the results of the two different systems with those of exponential smoothing and random walk on seven different error measures. The results show that pattern recognition based approaches in time-series forecasting are highly accurate. Simple theoretical trading strategies are also mentioned, highlighting real applications of the syste

Pattern Matching and Neural Networks based Hybrid Forecasting System

Copyright © 2001 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Advances in Pattern Recognition — ICAPR 2001Second International Conference on Advances in Pattern Recognition (ICAPR 2001), Rio de Janeiro, Brazil, March 11–14, 2001In this paper we propose a Neural Net-PMRS hybrid for forecasting time-series data. The neural network model uses the traditional MLP architecture and backpropagation method of training. Rather than using the last n lags for prediction, the input to the network is determined by the output of the PMRS (Pattern Modelling and Recognition System). PMRS matches current patterns in the time-series with historic data and generates input for the neural network that consists of both current and historic information. The results of the hybrid model are compared with those of neural networks and PMRS on their own. In general, there is no outright winner on all performance measures, however, the hybrid model is a better choice for certain types of data, or on certain error measures

A comparative study of time series analysis for forecasting energy demand in Nigeria

The complexity of the Nigerian Power Sector due to industry deregulation and abrupt variations in electricity demand and the ever increasing population density requires urgent research attention and industry action. Getting an accurate model to fit the energy demand pattern has become imperative and the need for an appropriate model cannot be overemphasized. This article therefore presents a comparative study on time series modelling of average and peak load forecasting in Nigeria. Data from the National Control Center (NCC), Oshogbo was harvested and analyzed using Harvey, Autoregressive, Moving Average and Exponential Smoothing Time Series Models while using R-Statistics for model simulation. The comparative performance showed that the Harvey Model best predicted load demand with least values of 111.0188 Root Mean Squared Error, (RMSE), 9.1095 Mean Absolute Error (MAE), 2.3579 Mean Absolute Percentage Error (MAPE) and 0.0156 Theil Inequality Coefficient (TIC) for average load and 117.4345 RMSE, 10.183 MAE, 17.01 Mean Absolute Percentage Error (MAPE), and 0.015 TIC for peak load.Keywords: Forecasting, Electric load demand, Harvey model, Autoregressive model, Moving Average model, Time Series model, Exponential Smoothing model, Theil Inequality Coefficien

Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

A comparison of univariate methods for forecasting electricity demand up to a day ahead

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives

A SARIMAX coupled modelling applied to individual load curves intraday forecasting

A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARIMAX models and to take advantage of the usual linear relation between energy consumption and temperature for thermosensitive customers. We first recall some issues related to individual load curves forecasting. Then, we propose and study the properties of a dynamic coupled modelling taking temperature into account as an exogenous contribution and its application to the intraday prediction of energy consumption. Finally, these theoretical results are illustrated on a real individual load curve. The authors discuss the relevance of such an approach and anticipate that it could form a substantial alternative to the commonly used methods for energy consumption forecasting of individual customers.Comment: 17 pages, 18 figures, 2 table