Paper: Textual Data for Time Series Forecasting
Whether it is in the field of energy, finance or meteorology, accurately predicting the behavior of time series is nowadays of paramount importance for optimal decision making or profit. While the field of time series forecasting is extremely prolific from a research point-of-view, up to now it has narrowed its efforts on the exploitation of regular numerical features extracted from sensors, data bases or stock exchanges. Unstructured data such as text on the other hand remains underexploited for prediction tasks, despite its potentially valuable informative content. Empirical studies have already proven that textual sources such as news articles or blog entries can be correlated to stock exchange time series and have explanatory power for their variations BIBREF0, BIBREF1. This observation has motivated multiple extensive experiments to extract relevant features from textual documents in different ways and use them for prediction, notably in the field of finance. In Lavrenko et al. BIBREF2, language models (considering only the presence of a word) are used to estimate the probability of trends such as surges or falls of 127 different stock values using articles from Biz Yahoo!. Their results show that this text driven approach could be used to make profit on the market. One of the most conventional ways for text representation is the TF-IDF (Term Frequency - Inverse Document Frequency) approach. Authors have included such features derived from news pieces in multiple traditional machine learning algorithms such as support vector machines (SVM) BIBREF3 or logistic regression BIBREF4 to predict the variations of financial series again. An alternative way to encode the text is through latent Dirichlet allocation (LDA) BIBREF5. It assigns topic probabilities to a text, which can be used as inputs for subsequent tasks. This is for instance the case in Wang's aforementioned work (alongside TF-IDF). In BIBREF6, the authors used Reuters news encoded by LDA to predict if NASDAQ and Dow Jones closing prices increased or decreased compared to the opening ones. Their empirical results show that this approach was efficient to improve the prediction of stock volatility. More recently Kanungsukkasem et al. BIBREF7 introduced a variant of the LDA graphical model, named FinLDA, to craft probabilities that are specifically tailored for a financial time series prediction task (although their approach could be generalized to other ones). Their results showed that indeed performance was better when using probabilities from their alternative than those of the original LDA. Deep learning with its natural ability to work with text through word embeddings has also been used for time series prediction with text. Combined with traditional time series features, the authors of BIBREF8 derived sentiment features from a convolutional neural network (CNN) to reduce the prediction error of oil prices. Akita et al. BIBREF9 represented news articles through the use of paragraph vectors BIBREF10 in order to predict 10 closing stock values from the Nikkei 225. While in the case of financial time series the existence of specialized press makes it easy to decide which textual source to use, it is much more tedious in other fields. Recently in Rodrigues et al. BIBREF11, short description of events (such as concerts, sports matches, ...) are leveraged through a word embedding and neural networks in addition to more traditional features. Their experiments show that including the text can bring an improvement of up to 2% of root mean squared error compared to an approach without textual information. Although the presented studies conclude on the usefulness of text to improve predictions, they never thoroughly analyze which aspects of the text are of importance, keeping the models as black-boxes.
Question: How big is dataset used for training/testing?
Answer: 4,261  days for France and 4,748 for the UK