Questions tagged [time-series]

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

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203 views

Train MLP Neural Network on time series data?

Newbie question here but I was curious to ask if an MLP Neural type network can be trained on time series data? The dataset that I have is an electricity type data set from a building power meter and ...
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31 views

Synchronizing timestamps between multiple sources of time-series data that are both asynchronous and imprecise

I have time-series data coming from multiple sources. The data from each source arrives at a "roughly known" interval (i.e. once per hour), but the timestamps across the sources are not ...
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1answer
84 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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3answers
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Multi-Source Time Series Data Prediction

I was wondering if anyone has experience with time series prediction for data from multiple sources. So for instance, time series $a,b,..,z$ each have their own shape, some may be correlated with ...
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1answer
289 views

Forecasting vs non-forecasting predition for time series anomaly detection

I have got the objective of implementing a uni/multivariate online anomaly detection system. After multiple days of research, I could collect many ways to achieve this (Eg. moving average solutions ...
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1answer
47 views

Regression with LSTM network: use multiple time series as input

I've spent a few days on this and am starting to think I'm missing the obvious solution as this doesn't seem like a very uncommon problem. As an example dataset: I have 100 measurements with each a ...
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25 views

How to Classify Game Stages Based on Bitrate Time Series Data

I need suggestions for my project and would be glad if you would give me a hand. I have a dataset of frames obtained from the old-school game DOOM. Each frame in the dataset has the following columns: ...
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1answer
75 views

What are the differences between IBM BlueMix and IBM Data Science Experience?

This may seem like a silly question, but as I am going through the documentation for both services it is difficult to disentangle what each does, specifically. From what I've gathered BlueMix is ...
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24 views

Neural net performance using rmse

I am trying to build a NN which can predict exchange values. I am quite new to R and NN and I don't quite understand how I could improve the performance metrics of the neural network. I have tried ...
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1answer
62 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
34 views

How can I plot a line for time series data with categorical intervals in R

I am working with single time-series measurements that I want to plot for the time window of about 1 week. This is the data I am working with. This is my R script: ...
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1answer
29 views

I'm trying to do a time series model without a datetime field in python. Is this possible?

I have a dataset with data like this: Day Revenue 1 1.2 2 1.5 3 1.1 4 1.34 I want to do a time series model on it, but am ...
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1answer
690 views

Binary classification with time-series features

I have the following time-series features: Diastolic Blood Pressure, Systolic Blood Pressure, Heart Rate, RR variability and Arterial Blood Pressure. Each of these clinical parameters was measured for ...
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21 views

How to put marker on time series training set

My input is this picture And I would like to put markers on it and use time series with markers as a label Two picture are not scaled. Main point is I would like to train RNN classifier and let it be ...
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10 views

Do Any Frameworks Provide Better Support for End-To-End Integer-Based Feature Engineering, Modeling, and Inference?

A retail enterprise I work with with wants to switch from its home-grown time series data analysis and prediction system to something more established and with community support. One unique feature ...
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2answers
136 views

Is there any time series model which handles data at variable frequencies.?

Goal: Predict the yellow points.(yellow events appear at varying frequencies) But I'm struggling to find a good model to fit this use case. Most of the time series algorithms are handling data which ...
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1answer
43 views

Detect Missing Records in Dataset

I have a dataset that contains several measures from various widgets on a daily basis. While the widgets remain relatively stable over time, sometimes there are legitimate reasons for one to disappear ...
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1answer
70 views

Model for classifying time-series data with distinct features?

I've heard about time-series classification being done with TCN's and CNN's combined with LSTM's very often, citing that CNN's would provide insight both forward and in the past since you already have ...
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1k views

Data Conversion to Time Series in R

I am having Sales data of 2018 and 19. I need to convert to time series. The data is not having daily sales View(df) Sales Date 75606 11/01/18 95620 16/01/18 55666 ...
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17 views

LSTM with multiple entries for the same timestamp

I have a dataset where I have multiple entries for the same timestamp and I want to use LSTM to forecast the next timestamp given the previous 5 timesteps. From https://machinelearningmastery.com/...
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1answer
14 views

Correct LSTM model to predict shuffled data

For a year I've been collecting data from my RPi: [0 core load, 1 core load, 2 core load, 3 core load, environment temperature, fan speed, CPU temperature] Now I ...
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1answer
22 views

How to get periodicity from timeseries data?

I would like to create a recommendation system for a smart home application. I gather the data in a time-series database. The app monitors the on/off state of a smart lamp and can create daily ...
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0answers
17 views

Predicting sparse time series data

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0's, since there is no continuous flow of cars coming to charge but rather ...
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7 views

Modeling Scaled Residuals

I found a good model for a time series forecasting problem I have but it doesn't allow for covariates, which I need to include a few of (date-based events). The approach I came up with to deal with ...
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2answers
55 views

Input with variable length Classification problem

I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem). The dataset looks ...
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1answer
53 views

I want to know which machine learning algorithms can be used for trajectory classifications?

I am working on project for clustering of air objects based on their trajectories. Like I want to train a model on dataset of different flying object's trajectories so later I can predict what type of ...
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1answer
99 views

Help improving time series prediction with LSTM on PyTorch

So, I am trying to use a LSTM model to forecast temperature data on PyTorch. I am relatively new to both PyTorch and the use of recurrent networks so I took a model I found on the internet to start. ...
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1answer
118 views

Grad_CAM for time series

I am new to deep learning and trying to build a Grad-cam from time series data. Shape of my input sample is (188,1), its an ECG signal and I have a cnn-1D model for classification. Keras provides ...
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1answer
44 views

Revenue Projection

Given that we have Monthly revenue data for pass 3 years (36 rows of revenue) We have other data including economic indicators, industry indicators as well (other columns in the 36 rows) ...
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2answers
55 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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0answers
19 views

Embedding a categorical variable and concatenating with a numerical variable, in a many-to-one sequence problem with multiple features

I have a small data set where I track 4 variables across 4 time periods, 1 categorical and 1 numerical variable. Below is picture of data set that I am using: cat1 - Categorical variable encoded num1 ...
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9 views

Time series packing algorithm for load balancing/smoothing

I'm looking for pointers to implement an algorithm which takes many data series (load profiles) and packs them into a fixed number of containers in order to minimise peaks and achieve the most even ...
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1answer
30 views

Using Low Frequency Labels with High Frequency Features

I am trying to build a model (most likely a regression or random forest regression) for quarterly financial data. My training data has a daily cadence, but I am not sure how to work with these to ...
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10 views

How can I intuitively calculate the accuracy of my financial prediction model?

I've built a SARIMAX model based on my personal spendings record as a college thesis and have reached a point where I'm pretty content with how it turned out and am getting ready to start writing the ...
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3answers
964 views

Detect the time at which deviation occurs in time series data

I working on multivariate time series data. I have sensor data generated by a machine every time it is operated. Data set consists of machine_ID(machines of same model), hours_ operated, measurements ...
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1answer
102 views

Negative R2_score Bad predictions for my Sales prediction problem using LightGBM

My project involves trying to predict the sales quantity for a specific item across a whole year. I've used the LightGBM package for making the predictions. The params I've set for it are as follows: <...
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3 views

Supply Chain forecast with Covid-Data

I am working for a large food retail company and we are using ML models to predict the demand of certain products for the weeks to come. Of course, looking at the sales distribution, 2020 was a ...
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1answer
122 views

How do I use rnn to forecast to n periods with limited data?

So this is my 1st time trying to run a small time-series dataset through an RNN, but after a lot of searching, I haven't been able to find, 1. How I can use this to forecast to n periods ? (like in ...
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1answer
74 views

LSTM's for timeseries with additional regressors

I have a dataset consisting of the weekly sales of 3,000 stores over the past 5 years, and have constructed a LSTM to forecast the next year of sales, given the previous year of sales. At each ...
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0answers
27 views

How to calculate MAE and threshold in a multivariate time series

I'm trying to understand how to calculate the MAE in my time series and then the thresholds to understand which of my data in the test set are anomalies. I'm following this tutorial, which is based on ...
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1answer
23 views

Problems with Concatenating Embedded Categorical and Numerical variables for LSTM use

I am new to here and new to Deep Learning too, so apologies in advance for any ill formatted code or wordings. I have a data set where I track 4 variables with 2 categorical and 3 numerical fields, ...
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2answers
509 views
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11 views

How to decide the right granularity in time series?

I am wondering how to decide right granularity I should use for time series if I have data for hourly as well as daily and given that I don't have business constraint which asks me to do forecasting ...
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1answer
87 views

CV Pipeline for Time Series with Differing shape for X and y

I am playing around with ML models to forecast a time series. I'd like to generate a sklearn pipeline like ...
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1answer
111 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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2answers
107 views

Autenocoder and anomaly detection task

I'm trying to create an autoencoder for the anomaly detection task, but I'm noticing that even if it performs very well on the training set, it starts to stop recreating half of the test set. I tried ...
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1answer
80 views

How to Prepare data for LSTM

I'm having difficulties to wrap my head around how I can prepare my dataset to train an LSTM. Below is a screenshot of a subset representation of my dataset. There are several other feature not ...
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1answer
72 views

Predicting credit applications with timeseries

I was wondering what the best way is to make a model for predicting credit applications. I have two tables, which look like this: ...
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0answers
14 views

Bidirectional LSTM usage with sensor data

I am applying a deep LSTM network in order to classify time-series data from different sensors. In the field (energy) I often see the research using bidirectional LSTMs for forecasting. I don't get it ...
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1answer
182 views

Feature selection for time series prediction

I'm working on an LSTM-based stock market forecasting problem and trying to figure out a way to select input variables. When calculating correlation between variables (e.g. Close price of Tesla vs ...

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