Questions tagged [time-series]

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

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39 views

Forecasting using Boosting methods on Non-stationary Time Series data

Theoretical Noob question - Can we use boosting methods to effectively forecast the future after being trained on a non-stationary time series? Or do you train/fit on the residual of the training set ...
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1answer
12 views

Architectures that take inputs of mixed sampling rates

Let's say a model is trained on multiple datasets of 1D time series. These datasets have been gathered with different sampling rates. I plan to use a convolution neural network to process these time ...
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1answer
110 views

ARIMA training super slow

I am fitting ARIMA model (from statsmodels) on 20 000 elements dataset on a 24 CPU 200+GB RAM cloud server for over 24 hours now. It loads all the CPU's. But It takes so long... Is it how it works or ...
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14 views

Is there a way to calculate the cosine distance between 2 time series?

Let's say I have time series data of City A, City B, City C & City D that looks like this: ...
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1answer
14 views

(Labeled, if possible) time-series datasets for anomaly detection [closed]

I would like to create a big list of available time-series datasets for anomaly detection. I'm especially interested in the following: The time-series data should be segmented into cycles Ideally, ...
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1answer
18 views

How to detect time for the future events in time series data?

I am dealing with IOT data from a mechanical machine. On the input I have ~100 features that are measured every minute. On the output, I have labels of zeros and ones, where zero indicates the absence ...
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35 views

How to predict future prices with Keras LSTM time-series prediction model?

I have a trained and tested LSTM model which is meant to predict Ethereum close prices using all time csv data (24h steps). How do I now go about inputting an empty dataframe with future dates to ...
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25 views

Rolling window on uneven time series classification

I have a univariate time series data that I would like to take about 60 seconds of, extract features using tsfresh and classify into multiclass. So I might end up with a dataframe like: ...
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13 views

Prevent model from predicting the same examples for different inputs

I have a ANN model, that predicts a fixed length curve. The problem is, those curves are really similar to each other, for example these two: To compare those curves, I use RMSD between their points....
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10 views

Modelling probability of switching of label from 0 to 1 in next time period for multivariate time series data

I have a data set consisting of unbalanced panel data, i.e. longitudinal multivariate measurements on multiple individuals. I want to estimate a probability that the individual will become 1 in next ...
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1answer
30 views

Creating features from raw accelerometer data

I have a dataset containing raw 3-axis accelerometer data collected from a users lower leg and I want to create a classification model (as simple as possible) that detects if the user is sat down or ...
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8 views

Series Through a GPU's Window To, For Each Item, Output a Prediction and Retrain?

Perhaps I'm missing something obvious but I've not run across a Keras or PyTorch example of online training and series prediction loop implemented on a GPU with these (seemingly obvious) ...
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How to cluster large time series?

I want to perform clustering on a dataset which has a few thousand stock prices time series, each one with daily prices for previous 5 years (a couple thousand days). What would be the best approach ...
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1answer
26 views

Terminology of time series

The terms Time Series Analysis, Time Series Forecasting, and Time Series Modeling are widely ...
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2answers
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Time Series data visualization

When I visualize data using matplotlib it displays very well, but when using Plotly, the data display very bad.
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Correcting for seasonality on multiple timelines

I have many time-series of economic data, broken down quarterly. There is always an element of seasonality in each timeline, but it can very wildly. See the below charts: I'd like to correct for ...
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Time series forecasting with constraints

I want to predict the passenger flow volume of an airline route, which subjects to supply capacity constraints of the route (i.e., the passenger flow volume should not be higher than the supply ...
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How far into the future can I forecast a time-series with an LSTM and strongly seasonal data

I am working on a Sequence-to-Sequence + Attention model for some time-series data. Now I have a really long time series, basically 40 years of daily observations for multiple sensors. The data itself ...
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1answer
36 views

PyTorch: LSTM for time-series failing to learn

I'm currently working on building an LSTM network to forecast time-series data using PyTorch. I tried to share all the code pieces that I thought would be helpful, but please feel free to let me know ...
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8 views

Forecasting monthly visitor count from daily values

I have a dataset of the daily visitor count of a website. Given this information, I want to forecast what the monthly visitor count will be. Depending on the visitor count on a day of the month, I ...
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1answer
72 views

Calculate the similarity between pairs of time series data

I have 5 pieces of time series data. It is the weekly sales of 5 different stores (A,B,C,D,E). There are no missing values. A quick visual inspection shows that these 5 pieces of time series data have ...
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6 views

Audio generating models

I was looking for an implementation of audio\timeseries generation models. I want a model that utilizes time series as well as a frequency map (such as log-Mel or STFT). My goal is to generate a time ...
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7 views

how to codify date-time feature in a dense vector?

if we have datetime as a feature for our model in time-series analysis (e.g. trading analysis ...). As you know any datetime-based features have two characteristics: cyclic linear For instance if we ...
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1answer
41 views

Segmentation of time series into one-second non-overlapping windows [closed]

I need to split the time series dataset (accelerometer values (timestamp, X, Y, Z)) into segments in the form of a one-second window that does not overlap. I am trying to find an example of its ...
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23 views

Multiple (>1500) timelines- which model to use?

I'm new to Data Science (<6 months). I'm currently working with American economic data from the BLS. I only have quarterly numbers for about 20 years, so my timeline data isn't very wide- but I do ...
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40 views

AR coefficients are not stationary

I have a timeseries data and I want to forecast it by applying ARIMA. After reading data, I decomposed it to analyze its components and get an idea whether it is stationary or not. It seems there is ...
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1answer
56 views

LSTM model prediction is almost constant

I am new to RNN and LSTM and currently experimenting with different settings. When trying to model time series data in absolute terms (predicted close price), I am faced with the following problems: ...
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1answer
22 views

Predicting Player Position from pervious positions and LSTMs

I am trying to use LSTMs to predict player positions in a field game. I try to overfit 8 slightly different time series. For this overfitting task I just use the positions of the players. A data ...
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1answer
18 views

Signal correlation - matching specific points

Question: What are some recommended techniques for matching specific patterns in data sets? Background I have several thousand sites for which I have collected time series data. In the example image ...
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Autocorrelation of two functions multiplied and raised to arbitrary powers

Given a signal $A$ and a signal $B$ with autocorrelation times of $\tau_A$ and $\tau_B$, respectively, where $\tau_A > \tau_B$, is there any general statement that can be made about the ...
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1answer
17 views

Time series prediction with Lstm on patients data

My data includes different time series length (depends on number of exams each patient did) as well the interval between exams is different. How can I run lstm on this kind of data? (Where the ...
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11 views

Using dynamic time warping (DTW) to match only one point in reference series?

In a standard implementation of Dynamic Time Warping (DTW) or fast DTW such as those here and here every point in the two series is matched. Question: is there a requirement for every point to be ...
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35 views

How to account for rare events at different time intervals while using LSTM neural networks?

I'm working on an interesting sequence-to-sequence (regression) time series problem where some static features/rare events can change the behavior of future time series. The problem is a forecasting ...
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8 views

Why are my evaluation stats giving weird results when training a neural network?

I am training an RNN which uses LSTM layers on stock data. Upon training and getting the evaluation stats, I get the same stats almost every time, which are very bad. Here is what my stats look like: <...
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12 views

Bayesian Network Modeling on time series data with constant discrete features

I'm trying to model a dynamic bayesian network that can infer relationships between traffic sate of different road links over time. The main theme of mystudy is to model the relationship of change in ...
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Transfer learning from great labelled time series data to one with low quality labelling

I have a source dataset containing outputs from a sensor per minute and have made extra effort to label them correctly for approx. 3 weeks. I trained CNN-BLSTM network on that dataset which classifies ...
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1answer
453 views

Difference between sequence length and batch size in time series forecasting

I am using Keras for time series forecasting and I am trying to understand the tutorial on the offical site of keras about time series forecasting that you can find here (https://keras.io/examples/...
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1answer
42 views

How to split data into training, validation, test data sets if the data is non-stationary?

When splitting data into training, validation, test data sets to be fed to machine learning model, the data is ideally expected to be stationary. However, in the real world, some data is non-...
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1answer
32 views

Is it possible to update data and retrain just one of several data series in bigquery model

I am building something very similar to this BigQuery ML example project. My system is different in two ways: Firstly it will need several thousand time-series so I would prefer to use the multiple-...
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1answer
156 views

Azure automl time series forecasting error

I'm using Microsoft Azure automl to try and generate models for time series forecasting but I keep getting an error: ...
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0answers
36 views

How to predict out-of-sample observations with depmixS4 package in R?

I have a series of univariate data and I want to fit a Hidden Markov Model on it using the depmixS4 package on R. My final goal is to predict the next k observations (let's say k = 10) for the data ...
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1answer
18 views

Suggestions for improvement? Time series of variation in relative frequency of emotion-related words in academic psychology over time

First time plotting and interpreting time series data and I have used a line plot for ease of use. I am aware this is incredibly basic, but any input/ recommendations would be much appreciated (e.g., ...
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1answer
23 views

Finding Correlations in two Datasets

I have an assignment where I am trying to find correlations between Lightning Strikes and Telecommunication damage. The two datasets consist of many columns (especially the human-recorded ...
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1answer
46 views

Encode time-series of different lengths with keras

I have time-series as my data (one time-series per training example). I would like to encode the data within these series in a fixed-length vector of features using a keras model. The problem is that ...
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1answer
26 views

Long range forecasting with sequence-to-sequence models

I have a task where I want to forecast daily observations for 1 year or 2 years in advance at multiple locations--so 365 or 730 days in advance. I actually have a pretty good dataset, meaning daily ...
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77 views

LSTM giving almost constant output

I have used an LSTM with 4 layers deep each layer having 10 LSTM units to predict the AAPL stock 500 steps away by looking 50 steps back and it was predicting well (only a lag was there). However when ...
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12 views

selecting only a certain number of top features using tsfresh

How can I select top n features of time series using tsfresh? Can I decide the number of top features I want to extract?
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1answer
32 views

Similarity between two time series with different sampling frequency, different amplitude, and different lengths but taken from the same source?

I have two files with accelerator readings and I want to get some metric/ measurement to get the similarity between these two files. I have tried Pearson’s R coefficient, dtw distance, dtw score. ...
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1answer
14 views

After training and saving a model can we give more information as input?

Suppose my data is a time series with multiple features such as wind, temperature, holidays, etc.. and I'm predicting a target variable Y. After I go through the whole process of splitting data into ...
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1answer
73 views

How can I generate reasonable dummy/artificial data from a pre-existing time-series data?

I have a dataset like this, basically all numerical time-series data. I would like to generate dummy/artificial/fake data for future values of this, preferably in python. How can I achieve this for ...

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