Questions tagged [time-series]

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

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3answers
11 views

What is the minimum requirement for the dataset for time series forecasting?

I have a dataset of patients, and for each patient a measurement is taken 3 times per day. For example, Patient 1 has recordings at 7.30 am, 12.30 pm and 8.30 pm. Patient 1 has a collection of 30 days ...
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10 views

Excel date-time auto fill error

I have data of 10 metrics for a 24 hour period which is in 5 minute intervals. I merged all the 10 data metrics into one file and converted it into a .xlsx file. After opening the excel file I ...
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6 views

The optimal number of segments for Piecewise Aggregate Approximation (PAA)

I have time series dataset and I want to break it into segments, in order to run autoencoder on individual segments. After some research, PAA does what I need: it breaks the dataset into segments (...
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20 views

Multiple Event Identification in a Series (Segmentation or Event Extraction)

I've just started a project and I've having trouble even defining the problem! I'll try now and any help, no matter how small will be appreciated greatly. Scenario: Given a series (doesn't matter if ...
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15 views

How to compare methods for forecast of time series where the element of the series are vectors?

Suppose we have a series of vectors v1, v2,...,vn and we must forecast vn+1. We must confront various methods of forecasting (VAR,LSTM ecc). How can we choose a set of metrics in order to perform the ...
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1answer
16 views

Time Series Classification for 1 hour blocks

I am doing some analysis on time series. The time series would consist of 3 channels and contain 5 minute interval data. What I want is to be able to give it a 1 hour block of 5 minute interval data ...
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9 views

Removing seasonality in time series by differencing a weighted average of the previous period

I have effectively removed weekly/daily seasonality from my time series by subtracting the same time interval a week before. This makes the series mostly stationary, which has been great for modeling ...
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25 views

Reshape Time series data for Conv2d Block

I am modelling my time series data into a supervised learning problem for the input to a conv2d block in pytorch from this tutorial. ...
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2answers
42 views

How to handle weekdays in a NN?

I want to test if using additional information of weekdays would improve my NN. Therefore, I just converted the weekdays numerically such as ...
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2answers
33 views

Pandas datetime error when reading from excel file

I am trying to read an excel file that has two columns using pandas. This is how the data looks in excel file: ...
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0answers
36 views

Forecasting future close returns

I have end-of-day time-series dataset with following data Ticker Date Open High Low Close Volume I have calculated end-of-day closing returns ...
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2answers
41 views

Understandable and explainable machine learning model

I want to find formula for best financial portfolio. Inputs: Historical fundamental data for last 15 years. For 3000 companies for every quatal we have things like ...
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1answer
67 views

How to update the posterior belief when we are observing a stream of correlated data from a fixed but unknown data source

I want to build a [probabilistic] model that aims to infer the true value of an unknown categorical variable, $y \in \{1,2,..., K\}$. We have a dataset $(X,y): \mathbb{R}^d\rightarrow \{1,2,..., K\}$ ...
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15 views

Can I use/modify an Autoencoder to handle missing data?

I am about to implement an Autoencoder to detect anomalies. Therefore, e.g., in my test set, there is a situation where the data stream broke for some days. This results in a lack of data and should ...
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3answers
40 views
+50

Detect geolocation match a GeoJson pattern

I'm trying to detect if a geolocation (lat, lng) match a GeoJson pattern. As example i have line of location points and i want to detect if a new point can match that pattern in certain radius, like ...
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0answers
21 views

Data Augmentation techniques for classification of imbalanced time series datasets

Now I have a task to classify the imbalanced time series datasets using ML classifiers, such as Logistic Regression, Decision Tree, SVM, and KNN. I am not allowed to use the Deep Learning tools, such ...
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14 views

Characterizing different parts of a time series data set

I generate time series data sets that often looks something like this: Given the series data (blue), I am trying to automate placement of "red" and "orange" levels to be similar to what is shown: ...
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Smoothing price according to sales volume

I was looking at the evolution of price for different products and I observed the following graphic As you can see the orange time series has a sudden change at the beginning of 2019. Therefore I ...
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27 views

Time series forecasting produce same values with different training data

I'm developing a python program which predict daily timeseries values. Each daily timeseries contains 288 values (a record every 5 minutes). The main idea is to train a LSTM model with 7 days data ...
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19 views

How to label time series data correctly for training RNN/CNN models?

My Case I want to tackle a deep learning classification task using various smartphone sensor data. I will use a self-built data acquisition app and basically walk around with the phone, manually ...
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7 views

models and algorithms to measure the difference and trend of a matrix sequence

I have a matrix sequence, i.e, a sequence consists of about 5000 matrix, each matrix are of the same dimension, e.g., 25*20. Are there any models/metrics to characterize the trend of these matrix. For ...
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0answers
18 views

Dropout noise shape when applying it on a series

I am training a neural network based on the Deep Sets framework (https://arxiv.org/abs/1703.06114, https://arxiv.org/abs/1810.05165). The basis of this approach is that one has a series of input ...
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1answer
29 views

How do I assess whether two time series change together?

In this example, at timepoint 5, both signals move up together. I would like to quantify these similar movements, and ideally disregard the parts where the signals are almost constant. What ...
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2answers
131 views

Time series forecasting dilemma. Could feature engineering overcome time dependency?

I keep reading articles about time series forecasting. They all start from the same assumption: time series forecasting can't be treated as a regression/classification problem. It is time dependent, ...
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1answer
33 views

Anomaly detection thresholds issue

I'm working on an anomaly detection development in Python. More in details, I need to analysed timeseries in order to check if anomalies are present. An anomalous value is typically a peak, so a ...
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0answers
15 views

Statistic that is responsive to changes in time series, yet not too volatile

Let's say I have a fairly volatile time series $X_t$ - it doesn't have any reason to show an upward / downward trend, but it does show drops and spikes from time to time. It can also change level (e.g....
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How to approach mapping families of vectors on a lattice and forecast resulting value

I describe here a model to describe how neighbours influence a node. I wish to implement it to attempt forecasting to values associate nodes; I post here asking for suggestions on mathematical model ...
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0answers
17 views

LSTM pulse signal prediction

i'm trying to capture long-term dependencies using LSTM, by creating a unit pulse signal every 62 points. The idea is to go back 62 time-steps and copy the value for the next time-step, so as to ...
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25 views

Does the add_regressor method on Facebook Prophet also work with categorical variables?

I went through the documentation of Facebook Prophet and was able to build a similar model for my time series dataset. The additional regressors I used were numeric. I achieved a reasonable MAPE score....
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1answer
19 views

Are time series algorithms immune towards collinearity?

I have a time series dataset with 63 features and a univariate dependent variable. This is my first major time series project, so I was wondering if algorithms like ARIMA and LSTM are immune towards ...
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1answer
19 views

hierarchical clustering doesn't work as expected

I have a precomputed distance matrix. I'm trying to do an hierarchical clustering using scipy: ...
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0answers
14 views

Temporal outlier Analysis on sensor data

I am working to find anomaly/outliers in sensor data using unsupervised machine learning (without training dataset). I have around 20000 samples taken per minute of various sensors. I just need to ...
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0answers
19 views

Any previous work to develop time series models for multiple label sets with different sampling rates

We have time series data with multiple labels: one label is sampled every day and the other label is added every one month and we would like to train the model accounting for both labels at the same ...
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2answers
34 views

Is autocorrelation the same as multicollinearity?

I have a time series dataset that has 63 features with traffic_volume as the target. ...
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0answers
5 views

Time Series and forecasting individual reservations

What kind of algorithm would best for following problem. I try to forecast reservation of different kind of tables. Let's say I have 100 different tables, which are reserved for from 17.00-22.00 ...
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8 views

Accelerometer and Gyroscope features

I am having accelerometer and gyroscope reading along x,y,z axis and want to get motion direction info at each time step. What all feature extraction would be best suited for this type of requirement. ...
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10 views

Generating correlated time series with different probability distributions

I have the following problem to solve: I need to generate two time series, each modeling a variable (lets call them VarA and VarB). For each of these variables, I have a PDF (PdfA, PdfB) which belongs ...
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0answers
13 views

Choosing a distance metric and a clustering algorithm for time series

For every entity I have a corresponding time series which is built by a sliding window (win_size=7d, win_shift=3d, so we have overlapped windows) With every win-shift, we count how many users are ...
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1answer
20 views

Is it common to add noise to Time Series data before training a model

I once read about somebody who added noise to their time series before training a model. They didn't write why they did it though. Is this common practice? If it is, why do people do it ie. to ...
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1answer
24 views

Time Series segmentation

I have a time series graph that is segmented into a few parts based on the maintenance day. You can think of it like vertical lines appearing out of the x axis which symbolize maintenance at the date. ...
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0answers
17 views

How to generate sports tracking data using deep learning?

Data: I have a 2D Numpy array that contains tracking data for football. Each row has the (x,y) coordinates for all players + the ball. That's 22 players and 1 ball = 46 columns. The frequency is 0.1 ...
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1answer
28 views

Predicting churn - deal with missing dates in time series and improve modelling result

This is the follow up question for General approach on time series for customer retention/churn in retail. I have a time series of data in the following form: ...
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1answer
37 views

Getting monthly revenue predictions for outlets

I am often presented with a task of predicting monthly revenues of retail outlets. Say I have a training set of N outlets, each associated with a series of historical monthly revenues (target) and a ...
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1answer
23 views

Forecasting Consumption for Multiple Products for Multiple Regions

Came across a very interesting Real-World Time Series Forecast Problem. Can you please help me understand the right track to resolve the below Time Series problem: Input Data Sample: and we want to ...
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2answers
83 views

Determining if a time series is random

An example time series would be the stock market, which is sometimes described as a random walk. Over time, this is clearly not the case as it has essentially gone in one direction (up) with only ...
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1answer
41 views

General approach on time series for customer retention/churn in retail

I have a time series of data in the following form: ...
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1answer
20 views

Split time series by python or by keras?

In Python you can use TimeSeriessplit() to split a time series properly for training but you can also do the same(?) in Keras by TimeseriesGenerator. Which one is recommendable? And/or what are ...
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1answer
11 views

Cluster evolution over time

I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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0answers
9 views

LSTM based anomaly detection scheme too closely tracking long spans of anomalous points

I've built a time series anomaly detection process that accurately predicts the value at the next interval. However, when there are dozens of anomalous events in a row, the model starts to "catch up" ...
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0answers
62 views

Building a speech commands dataset for audio recognition applications

I'm working on a DL project to recognize (10 - 15) Arabic speech commands from a continuous stream of audio, and I want to create a dataset similar to Google's Speech Commands dataset. Fortunately, I ...