0
votes
0answers
16 views

Regression line fit for linearly increasing data with manual reset

I've a linearly increasing time series dataset of a sensor with value ranges between 50 to 150 on which I've implemented a simple linear regression algorithm to fit a regression line, and I'm ...
1
vote
2answers
183 views

Estimate ARMA coefficients through ACF and PACF inspection

I know that this is probably a question that's been asked plenty of times, but i haven't seen an answer that's both accurate and simple. How do you estimate the appropriate forecast model for a time ...
0
votes
0answers
15 views

How to correctly concatenate time series data

I have this shifted time series data. For one set consists of features from week1-5 and labels at week6. Another set features from week2-6 and labels and week7 and so on. I have like four sets of ...
0
votes
1answer
44 views

How do I detect state change in multivariate time series?

I have a multivariate time series . For each row in the data we have the values of inputs and a label for stability (0 or 1 ) . What are the algorithms that can detect the stability for an unlabelled ...
0
votes
0answers
13 views

Correlation between sensors

Background: A home wired with multiple sensors, measuring attributes like temperature, light, motion etc. In addition, a multitude of actuators that can carry out an action like opening a door, ...
0
votes
1answer
79 views

Machine-Learning algorithms for Forecasting

For work, I'm working on an app where you essentially forecast the failure rate of the overall machine through different factors such as the historical failure rates for the components used to build ...
0
votes
0answers
24 views

Supervised classification on different time series

I have 300 files, each file has a time series data with a class label(0 or1) for each data point.I want to build a classifier, which can predict the class of a new time series data. How should I ...
3
votes
1answer
311 views

Choosing the right forecasting technique

I'm currently attempting to forecast visitor data for stores. My dataset includes daily visitor totals of three years. Note that the dataset isn't complete (stores can be closed for a few days, etc). ...
0
votes
0answers
24 views

In which Data Stream Mining Algorithms do Damped Windows make sense?

For Data Stream Mining, especially in Document Classification, the most common ML algorithms are Multinomial Naive Bayes, Stochastic Gradient Descent and Ozbag (ADWIN). When looking at their ageing ...
0
votes
0answers
25 views

Calculate similarity of waiting times of users

Let's say I have waiting times(seconds) of users in web pages. ...
0
votes
0answers
32 views

Understanding Maximum Entropy Bootstrap (meboot) algorithm

The Maximum Entropy Bootstrap (meboot) let's you create random realizations of time series. I have tried to make sense of the description of the algorithm in H. D. Vinod's paper 'Maximum entropy ...
0
votes
0answers
29 views

Time series image data classification / video image classification

I am working on classifying video frames into two classes, positive and negative. e.g. if a particular pattern appears in a frame that frame will be classified into positive, otherwise negative. But ...
2
votes
3answers
100 views

Dealing with different time series data in Machine Learning

I am trying to create a stock market model based on fundamental variables for the US economy. I am using R. Some of the variables I am looking to include are: GDP, Unemployment Rate, Initial Claims, ...
0
votes
0answers
28 views

Literature for prediction models where each training example has a different amount of data?

This could be a machine learning question as much as a statistics question, but I think this is the best place to put the question. Here are three different examples of problems where each ...
0
votes
1answer
23 views

How to Build a Foresight System?

For a research project, I'm asked to find ways to build an economic foresight system. For example, for the production of cheese. We will have data about the market indicators, like price, demand etc. ...
2
votes
0answers
55 views

Time series prediction where each datapoint has a sequence

I am Computer Science major, and new to stats, so please bear with me and point me to the right direction if what I'm asking is pretty obvious. I have a dataset, where each data point consists of ...
2
votes
2answers
139 views

How to define train and test sets in financial time series for estimating machine learning parameters

After reading some material, I found few options for defining train and test sets: Just splitting with no change. Accumulating/moving window of train set. Leave a relatively small (warming) period ...
0
votes
0answers
58 views

Time Period Predictive Modeling

I have been implementing some classification algorithms (Naive Bayes, SVM etc) recently on the iris data sets to get head start into the data science field. I enjoy working on machine learning ...
0
votes
2answers
115 views

step by step tutorial for newbie

I'm looking to join the field of statistics and more exactly to forecasting. I'm a software developer and I just started playing with R. Can you recommend me some tutorials related to forecasting, ...
0
votes
0answers
45 views

Gather insights from quarterly financial forecast data

I am trying to analyze a quite large (~25,000 rows) dataset of financial forecasts. The forecasts are usually not derived from algorithms, but come from a large number of analysts who forecast the ...
2
votes
6answers
206 views

Good references for time series?

I am wondering if anyone has book references for time series. I would like something comparable (in popularity) to the 'ESL' or to 'Machine learning' from Murphy in the machine learning field. Does ...
0
votes
0answers
67 views

Time Series Ahead Prediction in Neural Network, Large Scale Iterative Training

I am having trouble in implementing neural network to predict N points ahead. My only feature is previous time. I used elman recurrent neural network and also newff. In my scenario I need to predict ...
8
votes
2answers
141 views

Coupling time series information from sources with multiple spatial resolutions/scales

I have many satellite raster images available from different sensors. From these, the coarser ones have a very abundant temporal resolution. The medium resolution rasters tend to have less acquisition ...
1
vote
1answer
252 views

Neural network for prediction

I am working on neural networks for a regression problem in R using packages like nnet, caret etc. I have split my data into ...
2
votes
2answers
129 views

How is Hyndman's explanation of proper Time Series Cross Validation different from Leave-One-Out?

Hyndman's great explanation of proper time series CV is at the bottom of the page in the following link: http://robjhyndman.com/hyndsight/crossvalidation/ Leave-One-Out illustration in the following ...
0
votes
1answer
130 views

Modeling time: Probability distribution over time?

I'm trying to model users' posting behavior during a day. Say we have a bunch of users, with the time they post tweets. Now, for each user, I would like to estimate the likelihood of he post a new ...
0
votes
1answer
153 views

How do you do time series cross-validation using python? [closed]

Also, any tutorials/blogs available that you are aware of?
1
vote
1answer
149 views

Time Series Modeling with Lagged Variables

I have a dataset with columns that represent lagged values of predictors. To illustrate with a simple example, suppose we had car sales data for 3 years and the only predictors available were income ...
1
vote
0answers
90 views

Overlap in time series training sets

I have a time series prediction problem where the aim is to forecast the average value of $y_t$ over the next $T$ periods, given all the information available up to point $t$. For example, I want to ...
2
votes
1answer
442 views

Hidden Markov model for event prediction

Question: Is the set-up below a sensible implementation of a Hidden Markov model? I have a data set of 108,000 observations (taken over the course of 100 days) and ...
2
votes
1answer
134 views

How to model time-series data in CRFSuite?

I recently came across the CRFSuite package for CRFs. Though, it is primarily used for NLP applications like POS tagging, i was wondering if I could use it to model time-series data as well? Have any ...
1
vote
0answers
97 views

Search algorithms for time series in machine learning [closed]

Apart from k nearest neighbour and its variants like fast K NN, what are the other search algorithms? I tried googling and reading but I did not get any effective answer. I am aware of binary search, ...
2
votes
1answer
335 views

Time-series machine learning methods and R packages

I am trying to determine how to use machine learning models such as for eg., random Forest with (non-financial) time-series data. Using an example, suppose we wanted to find based on monthly scores ...
0
votes
0answers
112 views

Time series classification

I am classifying a set of time series inputs after creating independent features from every $n$ samples and running machine learning algorithms. I get good accuracy based on many error metrics on the ...
14
votes
2answers
3k views

Using deep learning for time series prediction

I'm new in area of deep learning and for me first step was to read interesting articles from deeplearning.net site. In papers about deep learning, Hinton and others mostly talk about applying it to ...
1
vote
1answer
75 views

Online learning to maximize profit

I have a software which takes input as investment and gives the output as return on a particular stock. Now profit metric $x_i$ is defined as the ratio of return $g_i$ to maximum possible return ...
2
votes
0answers
40 views

Using sequential observations to perform online prediction

I'm trying to perform predictions from a sequence of events. My problem is this: Data collection: Suppose you can continuously observe a person sitting in a library. You take note of every time that ...
0
votes
0answers
46 views

Performing online prediction from sequential observations

I am trying to perform some predictions from a sequence of observable events. My problem can be abstracted like this: Data collection: Suppose you can continuously observe a person sitting in a ...
3
votes
1answer
186 views

Combine several softmax output probabilities

I would like to combine the outputs of five neural networks, each with a softmax output layer of three classes each. A typical, example output is shown below:- where Figure 1 is the output of model ...
2
votes
1answer
47 views

How to classify data having sub-instance features?

I am trying to use machine learning on some peculiar (at least for me) data. Usually, when I do machine learning I am use to have the data in this format: ...
2
votes
3answers
217 views

How to combine time-series based features with different frequencies

I have 3 features which I want to use in my classifier. They are all time-series data-based. However, they are all at different frequencies and there have different matrix dimensions. I was wondering ...
1
vote
1answer
120 views

How to improve forecasting accuracy?

I got some users' history data and generated some sequences of real numbers. The length of each sequence is between 15 and 25. What's more, I do not know whether these sequences have patterns and the ...
2
votes
0answers
61 views

Guidance on classification analysis

I am doing a project where I would like to predict some characteristics from large data set and I am expecting that I would should use some Machine Learning techniques, but not sure how to proceed. I ...
3
votes
1answer
937 views

First steps learning to predict financial timeseries using machine learning

I am trying to get a grasp on how to use machine learning to predict financial timeseries 1 or more steps into the future. I have a financial timeseries with some descriptive data and I would like to ...
0
votes
2answers
182 views

Time Series Similarity : Differing Lengths with R

I am experimenting with creating a distance matrix between time series for clustering and similarity searching. The main reference I am using is for the Similarity procedure in SAS (Paper). I would ...
2
votes
1answer
261 views

Forecasting optimization techniques in fantasy baseball

I am currently trying to build a better forecasting model for my fantasy baseball roster. I currently am using commonly accepted projected season statistics (ZiPS from Fangraphs) to determine the ...
6
votes
0answers
170 views

Link Anomaly Detection in Temporal Network

I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
2
votes
0answers
125 views

detecting circadian rhythm in a time series

I have a sensor that can detect minute changes in distance. It produces a time series. I would like to point it at people and detect things like their sleeping pattern. How would one build a system ...
0
votes
2answers
66 views

difference in training and testing procedure of model

Can anyone please tell me the difference in training and testing of a model. I have developed 5/6 different single pass online learning algorithm (ets, ets+, evolving fuzzy modelling, SOFNN, ...