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Questions tagged [time-series]

Time series are data observed over time (either in continuous time or at discrete time periods).

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One sided time series alignment with dynamic time warping - reference constraint

I want to align multiple time series sequences $T:=\lbrace t_1,\ldots,t_N\rbrace$ of varied length in time using Dynamic Time Warping (DTW), such that all vectors in $T_{warped}$ will have the same ...
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10 views

Loss function returns nan on time series dataset using tensorflow

This was the follow up question of Prediction on timeseries data using tensorflow. I have an input and output of below format. ...
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0answers
6 views

Looking for freely available labelled time series data sets for automated feature extraction and selection

I am looking for recommendations of big, labelled time series datasets that are freely available on the net. My aim is to apply and evaluate methods of automatic feature generation and selection, ...
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4 views

Truncating RNN due to limited resources

I am currently working with time series data and am able to fit it to a RNN in Keras. However, I am continually getting new data and it becomes computationally more expensive to train on all of the ...
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How to denormalize data used to train a RNN?

I am running the code defined in the "usage" section of this repo. I get the same output : Before training and testing, the data is normalized with the following code : ...
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1answer
18 views

Why does my LSTM perform better when randomizing training subset vs. standard batch training?

I am training a simple LSTM network using Keras to predict time series values. It is a simple 2-layer LSTM. I get the best performance when I train on subsets of the training set that start at random ...
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0answers
11 views

Online learning of time series signatures [closed]

What's the current best in class method for one shot or online learning of signatures or repeating patterns in a continuously valued time series? I'm especially interested in methods applied to real-...
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1answer
19 views

Generating a set of different scenarios based on some initial observations

I have a in my hands 3 different time series which model 3 different scenarios (base, downside, upside). Every of this time-series depends on a set of 11 different attributes, which take values for ...
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1answer
45 views

Prediction on timeseries data using tensorflow

I have an input and output of below format. (X) = [[ 0 1 2] [ 1 2 3]] y = [ 3 4 ] Its a timeseries data. The task is to predict the next ...
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Machine Learning Model [closed]

I have a dataframe with columns as Month,Number of Demand, SkillsList,Location,Experience Grade,Practice , Skill Group and Demand Source. I want to predict the Number of demand by month. Which is the ...
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2answers
48 views

How can I detect significant changes in my data?

I have a dataset like this: ...
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1answer
22 views

Cumulative sum of increase/decrease over time [closed]

I am looking for an efficient way to handle a calculation. I have lots of timestamped events, representing loading and unloading of trucks. I need to construct the filling rate of these trucks over ...
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18 views

Time-series clustering Quality Measures

I am clustering time-series datasets which are not labeled (No Ground truth) and I want to measure the quality of the clusters. Could you please suggest any Clustering performance evaluation methods ...
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28 views

What kind of algorithm should I use to build ML model that can predict just next reoccurence of an event in the future (at irregular time interval)?

I'm quite new to machine learning and statistics. I've a dataset from some ecommerce sale's history. It's almost 2k instances, and features include personId (string), productCategory (string/...
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1answer
17 views

which forecasting models could be chosen?

I'm new for data analysis. I got some data from the regional environmental center. Measurements: Datetime, PointID, SubstanceID, Value (substances concentrations in air), MeteoID ,NextValue ...
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How to use K-Means to detect users anomaly in Access Control

I'm currently working on access control project, Smart Lock to be more spesific. Like the other smart lock system, the system required user's authentication to open the door. I'm using RFID as ...
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0answers
23 views

Any tool that can help on manually label a time series data please?

i am working with a 10 years weekly financial time series data set, which has the standard format date open high low close volume. i would like to manually label (classify) the data set with label/...
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9 views

Using by statement in proc esm

I was using proc een in SAS, for time series forecasting. I want to include two variables in by statement. My data contains four columns: Shop_id Item_id ...
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0answers
24 views

Input shape for forecasting time series with Keras LSTM

For the last few days I have been trying to get familar with time series forecasting using LSTM in Keras. I have been mostly following tutorials on https://machinelearningmastery.com. Overall the ...
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10 views

Generate captions for time series data trends

I am trying to generate captions for time series data based on increasing/decreasing values. I have a column with values which change gradually over time (time horizon is irrelevant for now). When we ...
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0answers
34 views

Clustering Multivariate Time Series

I have 1000 time series data from different stations each has 8760 rows (point) and 13 columns (variables). A sample time series is as follows: ...
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0answers
11 views

Inserting a feature in the training set made of a target variable

I have a timeseries that contain the daily price of items based on the stores , the manufacturers , and other attributes. Other than what we know about not including a signal from target variable in ...
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0answers
20 views

Am I missing something about LSTM?

I have imbalanced data that I pass through a LSTM network. Here is part of my code : ...
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0answers
26 views

How to compare slope of two time series

Suppose we have the 2 sets of time series data L1,M1 sampled at the same 1000 points of time t[t1,t2,...t1000]. Lets assume before hand that that the L1 can be given as L1 = tX+a where X is the slope ...
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1answer
8 views

Fully endogenous models for predicting multivariate time series

I have a formal social science background but I am new to data science. My interest is in building predictive models for applications in the social sciences, mostly (but not only) in economics. I am ...
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1answer
15 views

Using sensor data and a know reference point infer the position of a moving robot

Say, the robot is starting at a known position and I've data coming off of the robot as it traverses the grid layout. Exploiting the nuances captured in the data - like the implication of unequal rpm ...
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0answers
15 views

Applicable method for the clustering of time-series consisting of multiple events

I'm currently dealing with a time-series clustering problem at work and I need help with picking/suggesting the right methodology. The problem is similar to clustering of users on a website based on ...
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0answers
36 views

Reconciling time-based data when data source clock drifts

How can I reconcile time-based data when the clock on the data source tends to drift and the data may be infrequently retrieved? I measured the clock to be about 30 minutes behind after 15 hours. ...
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0answers
20 views

ACF (Autocorrelation) estimation

I have two signals "Signal A" and Signal B" and I plot the autocorrelation for each signal with itself as shown below. How I can determine which signal has a stronger correlation with itself. To ...
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0answers
22 views

methods to find causality between 1000 time series

I have an eco-system of 100 applications which each are monitored by let's say 100 metrics publishing every 5 minutes. So my dataset has 10,000 time series. I want to build/learn the dependency graph ...
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1answer
65 views

LSTM for time series - which window size to use

I have a LSTM based network which inputs a n-sized sequence of length (n x 300) and outputs the next single step (1 x 300). The "raw" data consists of a few thousand semi-processed sequences of ...
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2answers
39 views

Is it acceptable for a forecasting model to predict moving averaged version of the data?

I'm working on a project in which I'm developing a precipitation forecasting model. When I try to predict the original data, the model (LSTM neural network) is not able to predict the peaks. This ...
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1answer
17 views

Time series on syslogs

Is time series model suitable for network syslogs considering the fact the messages are sequential and the messages are outputted as a result of dependency between themselves which can range from ...
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1answer
16 views

Higher frequency of time series benefits

We are setting up an experiment for a model that is able to predict the evolution of a time series in different horizons. One of the parameters to decide is the granularity of frequency of our samples ...
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1answer
55 views

LSTM future steps prediction with shifted y_train relatively to X_train

I'm trying to predict simple one feature time series data with shifted train data. The source looks like this: ...
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0answers
25 views

Transposing a dataframe (time-series)

I'm working on event-forecasting problem for a supermarket, and the data frame I have looks like (sorry for formatting): ...
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0answers
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Classification on time series items - choose not constant threshold

I'm dealing with a classification problem on a time series {time: t, value: y(t)} where, for each time t, my classification algorithm gives as ouput the probability that the y(t) belongs to class 0 or ...
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2answers
27 views

Multiple-output vs single-output NNs

I'm trying to build a 5 input-5 output model using LSTM, where all the outputs are the same features as the inputs, predicted in the future. My question is: is it better to build 5 models, each with ...
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1answer
18 views

predicition for a specific month

I am attempting to build a predictive model based on the past historical data. I have details of specific machine failure based on the past year data. I have data from some months of 2016 and from ...
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0answers
21 views

Dataset containing spatial and temporal features (built on a CNN model)

A dataset contains spatial and temporal features. It contains the time series data (2 min intervals) of the sections of a map. It is 320*480 (320 map sections and 480-time intervals). Each row ...
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1answer
25 views

Training network with variable frame rate?

I would like to train a temporal network, but the video data available are in different frame rates(ex 7,12,15,30). How should I train this network, without down-sampling higher frame rate videos. I ...
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0answers
19 views

Can ARIMA be applied on a dataset of few months?

I have dataset of a few months of (time series) electrical load usage of multiple users. Can/Should ARIMA be applied for load forecasting considering that its not much data?
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25 views

RNN-based Predictions of Sine Waves with Frequency Different From Training Data

I am wondering if I can generate a sine wave with a frequency different from training data using RNN. For example, Using two training data of two time series, say 0[sec] ~ 10[sec] each: sin(t) and ...
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1answer
39 views

Timestamps in Ridge Regression Scikit Learn

I am trying to transform data for use in regression, most likely the Ridge or Lasso technique implemented in sklearn.linear_model. My training data contains time ...
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1answer
38 views

VAR model ValueError: x already contains a constant

I'm using VAR model for multivariate time series. The structure is that although each variable is a linear function of past lags of itself and past lags of the other variables, one and/or two of the ...
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3answers
58 views

Small dataset in Time series

I have soccer data with a time series index. 30 seconds interval. So, 194 rows for 90+ minutes per game. I have 1500 games. The dataframe has the following information. Home/Away: • Goal Total. • ...
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0answers
35 views

Multi-class time series data in LSTM

I have a data set that includes the individual performance of a number of "actors" over some period of time. For a less-than-ideal contrived example (I'm not able to share the actual use case), you ...
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2answers
83 views

Time-series decomposition to a base level and an effect of another feature

I've got a time-series data (let's denote it as y) and some feature (let's denote it as x). y...
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0answers
12 views

Exponential smoothing trend curve frequency

I want to predict week by week the numbers of orders. Data : all the year for 2013 to 2017 (52 weeks) and 25 weeks in 2018 Time serie plot : ...
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0answers
13 views

Predicting both - classification and regression with time series random forest

so I'm developing a sports book betting system. So my goal is to choose appropriate ML approach to predict client's next bet based on the history of other clients' bets. Its regression and ...