1
vote
2answers
40 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
23 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
21 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
41 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 ...
1
vote
1answer
62 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
46 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
99 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
42 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
191 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
39 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
128 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
185 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
104 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
102 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
107 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
134 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
69 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
290 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
101 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
89 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
238 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
97 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 ...
13
votes
2answers
2k 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
74 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
34 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
44 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
174 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
46 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: ...
1
vote
3answers
177 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
114 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 ...
0
votes
0answers
30 views

seeking advice on dimension reduction for spacial and time-series data

I have 200 data sets, each of them has roughly 600 rows with some exceptions (some have about 2000). Each data set represents data collected from a test subject, and the data in each one of the 200 ...
3
votes
1answer
639 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
157 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
204 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
127 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
117 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
64 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, ...
12
votes
1answer
562 views

How to predict one time-series from another time-series, if they are related

I have been trying to solve this problem for over a year without much progress. It is part of a research project I'm doing, but I will illustrate it with a story example I made up, because the actual ...
4
votes
2answers
237 views

Time series prediction with non-constant sampling interval

I have some data which can be modelled as such: each data sample $S$ is a series of discrete signal values $S(t_n) \in \{-1, 1\}$ measured at times $(t_{n, S})_{1 \leq n \leq N_S}$. The number of ...
2
votes
1answer
2k views

Example of time series prediction using neural networks in R

Anyone's got a quick short educational example how to use neural networks (nnet in R for example) for the purpose of prediction? Here is an example, in R, of a ...
2
votes
0answers
194 views

Anomaly detection in user behaviour using hidden Markov models

I would like to detect user anomalies or mal-behavior on a web site. For each user I monitor the web browser used, IP (and thus ISP & geo-location) of the user as well as users' activities on the ...
4
votes
2answers
351 views

How can I transform time series data so I can use simpler techniques for fault prediction?

I know this is primarily a statistics site, so if I am off-topic, please redirect me. I have a system with pumps that sometimes break and need to be replaced. I would like to be able to predict the ...
2
votes
1answer
192 views

Modelling longitudinal data

We have longitudinal data on children(n<20) in which we measure different quantities A,B,C,D (like distance walked, time spent in school etc.). These are all continuous variables. We measure these ...
0
votes
2answers
343 views

Predicting time series with NNs: should the data set be shuffled?

Suppose I'm trying to predict time series with a neural network. The data set is created from a single column of temporal data, where the inputs of each pattern are ...
4
votes
5answers
549 views

How to handle online time series forecast?

I have been dealing with the following problem. I have kind of a real time system and every time frame I read its current value, creating a time series (such as 1, 12, 2, 3, 5, 9, 1, ...). I'd like to ...
6
votes
3answers
546 views

Is it necessary to detrend and decycle time-series data when using machine learning methods?

For example: I want to forecast future values of a time-series based on previous values of multiple time-series' using a ANN and/or SVM. Inputs will be lagged values from each time series, and the ...
5
votes
4answers
1k views

Time series analysis with neural networks

I'm new to neural networks and machine learning and I was wondering how you use time series data to set the weights of a regular FNN, and how you use the ending weights to forecast the time series. In ...
1
vote
0answers
164 views

How to compare the accuracy of two different models using statistical significance

I am working on time series prediction. I have two data sets $D1=\{x_1, x_2,....x_n\}$ and $D2=\{x_n+1, x_n+2, x_n+3,...., x_n+k\}$. I have three prediction models: $M1, M2, M3$. All of those model ...
2
votes
2answers
202 views

SVM and non-linear predictive models - feature selection

Just throwing out a general question. What do people think of applying feature selection methods when using SVMs to build predictive models? I understand that SVM have built in regularization with how ...