Collaborating Authors


Forecasting: theory and practice Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Cluster time series data for use with Amazon Forecast


In the era of Big Data, businesses are faced with a deluge of time series data. This data is not just available in high volumes, but is also highly nuanced. Amazon Forecast Deep Learning algorithms such as DeepAR and CNN-QR build representations that effectively capture common trends and patterns across these numerous time series. These algorithms produce forecasts that perform better than traditional forecasting methods. In some cases, it may be possible to further improve Amazon Forecast accuracy by training the models with similarly behaving subsets of the time series dataset.

Modeling Combinatorial Evolution in Time Series Prediction Machine Learning

For instance, earthquake wave is the observation Time series modeling aims to capture the intrinsic factors underpinning of crustal movements, while different actions like running and observed data and its evolution. However, most existing studies walking will cause differences in observations of a fitness-tracking ignore the evolutionary relations among these factors, which are device. Moreover, in practice, we often observe the combinatorial what cause the combinatorial evolution of a given time series. For evolution of data; that is, the observed time series being covered example, personal interests are intrinsic factors hidden behind users' by the influence of multiple factors, and especially the relations observable online shopping behaviors; consequently, a precise item among these factors. For example, an earthquake is the result of recommendation depends not only on discovering the item-interest quick transitions from smooth movements in the Earth's crust to relationship, but also on an understanding of how user interests intense ones, which cause a sudden release of energy in the Earth's shift over time. In this paper, we propose to represent complex and crust. Meanwhile, observing one's online shopping logs, precise dynamic relations among intrinsic factors of time series data by item recommendations rely on tracing and understanding the shift means of an evolutionary state graph structure.

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning: Bharath Ramsundar, Reza Bosagh Zadeh: 9781491980453: Books


Reza Bosagh Zadeh is Founder CEO at Matroid and Adjunct Professor at Stanford University. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. Reza received his PhD in Computational Mathematics from Stanford University under the supervision of Gunnar Carlsson. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Boards of Microsoft and Databricks.

Special Edition Data Science Interview Questions Solved in Python and Spark: with Deep Learning and Reinforcement Learning bonus topics in Keras (BigData and Machine Learning in Python and Spark): Antonio Gulli: 9781534795716: Books


And why is it useful for BigData? 29 What is "continuous features binning"? What is a Standard Scaling? 38 Why are statistical distributions important? What is a Bias - Variance tradeoff? What is a training set, a validation set, a test set and a gold set in supervised and unsupervised learning? What is a cross-validation and what is an overfitting?

Deep Style Match for Complementary Recommendation

AAAI Conferences

Humans develop a common sense of style compatibility between items based on their attributes. We seek to automatically answer questions like "Does this shirt go well with that pair of jeans?" In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper. The basic assumption of our approach is that most of the important attributes for a product in an online store are included in its title description. Therefore it is feasible to learn style compatibility from these descriptions. We design a Siamese Convolutional Neural Network architecture and feed it with title pairs of items, which are either compatible or incompatible. Those pairs will be mapped from the original space of symbolic words into some embedded style space. Our approach takes only words as the input with few preprocessing and there is no laborious and expensive feature engineering.