Questions tagged [time-series-analysis]
The time-series-analysis tag has no usage guidance.
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Time series classification using multiples multivariate multi-length timeseries
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I would like to develop a time series classification algorithm to classify use a of parachute.
My data consist of multiple recording files (around 5min at 100hz, length of the recording can vary) ...
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Finding overlapping time under distance condition
I have a set of records (2 or more for each person) on multiple peoples locations (latitude and longitude) with timestamps.
each record has: person ID, latitude, longitude, timeStamp.
for each 2 ...
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why regression trees can be used in univariate time series forecasting?
I have been working on univariate time series data, and its very surprising for me that regression tree (DecisionTreeRegressor() from sklearn) works very well. But I don't understand the reason behind ...
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What type of neural network or machine learning technique would you apply?
I want to predict as many time steps of a variable (X) as possible. The more time steps forecasted, the more successful the solution proposed.
To the best of my knowledge, applying LSTM neural ...
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Finding the maximum sum series in time complexity of O(n)
You are given a list of prices (non-negative integers) $$ P=<P_1, P_2, ..., P_n> $$, where the prices $P_i$ appear in non-decreasing order.
Moreover, you have access to an oracle that can be ...
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Is there a good analogy between spectral representation of a signal and graph theory?
I am working on some time series problems where the Fourier representation of the signal in the frequency domain is also important. I am wondering if there is any connection between time series ...
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Optimize stacking time series by offsetting start times (feels like a backpack problem?)
Given a time-series of data collected from a single running process that takes 8 hours to complete:
Minute
GB of Disk Space Used
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8
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15
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22
...Etc. It is sampled every minute, for 8 ...
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Compare Hidden Markov Model's sample with ground truth data
I have a time-serie and I fit different HMMs on it, each with a different number of hidden states.
Now after sampling from the models , I'd like to compare the results with the ground truth data and ...
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Asymptotic growth of a series
How we can prove that:
$$
\sum_{k=1}^{c \log n-1}\:k\cdot \left(\frac{1}{2}\right)^{\frac{k}{3}}\in O\left(1\right) \quad \mbox{?}
$$
user130726
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Searching for pattern with Dynamic Time Warping
I'm creating app that allows user to authorize by drawing dynamic gesture in front of camera (actually Leap Motion sensor, but that's not the point). Every person can save their own gesture as their ...
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Remove features with too little variation
I have the following messy chart;
As you can see some features are very stable through the time series (post-2011) like Extraversion.
Is there an algorithm to remove features that do not have much ...
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an algorithm for detecting if noisy univariate data is constant or is sum of step functions
In an explicit algorithm I'm writing, there is a certain stage where I need to determine whether or not a certain noisy univariate data is constant or is sum of step functions.
For example, defining ...
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Detecting Events in time series [closed]
I am working with a time series which is, in fact a pressure signal.
There are special "events" in the signal that I am looking for.for example where the signal suddenly goes up or down or where there ...
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Which time series prediction techniques are useful given harmonic properties?
I have a time series dataset where events have harmonic properties, and seemingly the nature of the event's early segments can determine the remainder of the event (see example 1's oscillations). ...
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Algorithm for detecting a finite limit of time-series numbers
Is there a well known and proven algorithm to find the TOP (finite) limit of a set of points, which are time based metrics? I'm looking for an existing implementation, in order not to invent the wheel ...
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Constant time range sum query
I need to come up with a data structure that supports the following interface:
new(time_stamp x, value v): insert a new data v for time stamp x
update(time_stamp x, value v): update the data for ...
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How to add important events as input in neural network?
I'm quite new to neural networks so I apologize if this question is too basic/doesn't really make sense.
I have a financial time series dataset and I have a binary variable which is 0 if no important ...
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How to evaluate the learned prototypes for multivariate time-series (e.g. motion)?
Consider a method which finds prototypes for multivariate time-series (MTS) data, and is designed to find prototypes for each class of data.
For example, the {walking} class consists of some slightly ...
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Clustering non-overlapping time series
I have thousands of times series of different length and different time. I want to group them together so that I know the optimal ones to pick as input for a ML algorithm and to document how they are ...
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Using machine learning to detect trend reversal in a time series [closed]
I'm starting to learn machine learning, and have honed in on a problem to get my hands dirty with: how to detect a trend reversal in a time series.
I've asked this question on stats.SE more than a ...
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Is there an algorithm to "learn" the nonlinear relationship between variables?
Suppose those random variables are represented as scalar time series with nonlinear relationship (thus applying linear correlation or covariance would be futile) that may change over time, then is ...
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Finding a subarray of time series data in which all values are less than X for specified time Y?
The idea is to find an anomalous value (i.e. it's less than X) in time series data (sorted by time), and check if this behavior continues during specified time (Y=2 hours).
If all values in sub-array ...
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How can I fit a sine function to a SVR?
I have a time series which represent the sine curve. Now I want a regressor to learn this curve, and being able to predict for future values.
PS:
Sine curve is only for testing purposes. What I want ...
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Temporal alignment of two time series
I have two series of tuples
$$
A = \{(x_0,t_{01},t_{02}),(x_1,t_{11},t_{12}),\ldots,(x_n,t_{n1},t_{n2})\} \\
B = \{(y_0,tt_{01},tt_{02}),(y_1,tt_{11},tt_{12}),\ldots,(y_m,tt_{m1},tt_{m2})\}
$$
...
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Keywords for classification of 2D time series data?
Trying to find the right search terms for literature on classifying 2D time series data.
I am looking at data from positional tracking of a swarm of insects over time. I have example datasets for ...
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How to efficiently code Dynamic Time Warping algorithm with a locality constrain?
For given two lists $[s_1, s_2, ... s_n]$ and $[t_1, t_2, ..., t_m]$
I need to implement DTW algorithm with one extra constraint:
If $s_i$ is matched with $t_j$ then the next element $s_{i+1}$ has ...
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What is the fine line between time series data anlysis and time series data mining?
I am trying to do thesis work in Computer science and the data mining in time series is the relevant theme, however the analysis of time series is not (it is mathematics subject). What is the fine ...
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machine learning classification with financial instrument/time series data
I am new to machine learning and have started brainstorming some model ideas that involve financial instrument/time series data. I was thinking it might be useful to use a classification algorithm to ...
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Getting speed difference between signal comparison using Dynamic Time Warping
I understand that Dynamic Time Warping is an algorithm to find a matching between two signals with different length and speed
But is there a possible way to find the speed difference between the two ...
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detecting anomalies in time series data
I work on a web project that handles region based user submissions. We currently have about 50 regions, some receive a large number of submissions, some receive next to none.
We also ingest case ...
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Time Series Prediction with an LSTM
I have a time series that I want to predict with an LSTM. I am able to get very good results using 50 datapoints predicting 51, but I struggle to get any accuracy using something like 200 datapoints ...
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Machine Learning: Identify Patterns in Time-Series Data
I work in renewable energy. My company gathers a lot of data from equipment. This typically includes process data (such as transformer temperature, line voltages, currents, etc.) and discrete alarms (...
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Predicting next action to take to reach a final state
Does anyone know of an algorithm that could be used to determine the next action to take to reach a desired state when trained on time-series data?
For example, a robot starts at a certain state, ...
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Normalized measure from dynamic time warping
I am trying to find the similarity between two time series, but not in terms of distance, in something more sensible such as percentage of similarity. In other words I need something that shows the ...
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Levenstein distance and dynamic time warp
I am not sure how to draw parallel between the Wagner–Fischer algorithm and dtw algo.
In both case we want to find the distance of each index combination (i,j).
In Wagner–Fischer, we initiate the ...
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