Collaborating Authors

Time Series Analysis

Can We Forecast the Number of Sunspots?


Firstly: what is a sunspot? Sunspots are a temporary phenomena on the Sun's photosphere that appear darker than the surrounding areas. The reason why I have selected the sunspots dataset for time series analysis is sunspots appear on an 11-year solar cycle, meaning we should expect to see a seasonality component to the data. I will be modelling the seasonality trend using two different methods, the ARMA model and LSTM model. The data that will be used is from 1749 to 2013 and is the monthly average at each month.

DTWSSE: Data Augmentation with a Siamese Encoder for Time Series Artificial Intelligence

Access to labeled time series data is often limited in the real world, which constrains the performance of deep learning models in the field of time series analysis. Data augmentation is an effective way to solve the problem of small sample size and imbalance in time series datasets. The two key factors of data augmentation are the distance metric and the choice of interpolation method. SMOTE does not perform well on time series data because it uses a Euclidean distance metric and interpolates directly on the object. Therefore, we propose a DTW-based synthetic minority oversampling technique using siamese encoder for interpolation named DTWSSE. In order to reasonably measure the distance of the time series, DTW, which has been verified to be an effective method forts, is employed as the distance metric. To adapt the DTW metric, we use an autoencoder trained in an unsupervised self-training manner for interpolation. The encoder is a Siamese Neural Network for mapping the time series data from the DTW hidden space to the Euclidean deep feature space, and the decoder is used to map the deep feature space back to the DTW hidden space. We validate the proposed methods on a number of different balanced or unbalanced time series datasets. Experimental results show that the proposed method can lead to better performance of the downstream deep learning model.

An Ultimate Guide to Time Series Analysis in Pandas


It is the analysis of the dataset that has a sequence of time stamps. It has become more and more important with the increasing emphasis on machine learning. So many different types of industries use time-series data now for time series forecasting, seasonality analysis, finding trends, and making important business and research decisions. So it is very important as a data scientist or data analyst to understand the time series data clearly. I will start with some general functions and show some more topics using the Facebook Stock price dataset. Time series data can come in so many different formats. But not all of those formats are friendly to python's pandas' library.

Data Science in Layman's Terms: Time Series Analysis - CouponED


This course explores a specific domain of data science: time series analysis. The lectures explain topics in time series from a high level perspective, so that you can get a logical understanding of the concepts without getting intimidated by the math or programming. Whether you are new to time series or an experienced data scientist, this course covers every aspect of time series. The later half of the course entails several projects for you to get your hands dirty with time series analysis in Python. You will learn about modern time series forecasting models and AI, how to build them, and implement them to do extraordinary things.

What is an ARIMA Model?


The ARIMA model (an acronym for Auto-Regressive Integrated Moving Average), essentially creates a linear equation which describes and forecasts your time series data. Together, these three parts make up the AR-I-MA model. The AR and MA aspects of ARIMA actually come from standalone models that can describe trends of more simplified time series data. With ARIMA modeling, you essentially have the power to use a combination of these two models along with differencing (the "I") to allow for simple or complex time series analysis. Before digging deeper, I do want to note that the ARIMA model functions under some assumptions.

General Overview Of Time Series Data Analysis


In data science, a time series is a series of information points gathered in time order. Thus, it is a sequence of changes accrued at successive equal time intervals and obtained through observation over time. Because changes are dependent on time, as time increases, the changes will occur, increasing, decreasing or neutral changes. There can be many examples of time series like weather information of two or more years, stock market data, etc. Because in time series, information points are gathered at adjacent time-spaces, there is a relation between observations, whether they can be proportional or unproportioned.

3 Top Python Packages for Time Series Analysis


As a Data Scientist, you are employed because of your skill in data analysis and machine learning. One of the analyses often requested by the business is to do a business forecast, especially the time-related forecast.

ARIMA Model (Time Series Forecasting) in a Nutshel


Does your business struggle to understand the data in a better way or to predict future trends? Then you're not the only one in the business; many fail here. ARIMA can help you forecast and understand the new patterns from the past data using time series analysis. One of the top reasons why the ARIMA model is always in demand is that lagged moving averages smooth the time series data.

Time Series Analysis And Forecasting Using Python


You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right? You've found the right Time Series Analysis and Forecasting course. This course teaches you everything you need to know about different forecasting models and how to implement these models in Python. A Verifiable Certificate of Completion is presented to all students who undertake this Marketing Analytics: Forecasting Models with Excel course. If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it.

Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model Machine Learning

Network estimation from multi-variate point process or time series data is a problem of fundamental importance. Prior work has focused on parametric approaches that require a known parametric model, which makes estimation procedures less robust to model mis-specification, non-linearities and heterogeneities. In this paper, we develop a semi-parametric approach based on the monotone single-index multi-variate autoregressive model (SIMAM) which addresses these challenges. We provide theoretical guarantees for dependent data and an alternating projected gradient descent algorithm. Significantly we do not explicitly assume mixing conditions on the process (although we do require conditions analogous to restricted strong convexity) and we achieve rates of the form $O(T^{-\frac{1}{3}} \sqrt{s\log(TM)})$ (optimal in the independent design case) where $s$ is the threshold for the maximum in-degree of the network that indicates the sparsity level, $M$ is the number of actors and $T$ is the number of time points. In addition, we demonstrate the superior performance both on simulated data and two real data examples where our SIMAM approach out-performs state-of-the-art parametric methods both in terms of prediction and network estimation.