Questions tagged [forecasting]

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State of the Art/Research 2020 of Time Series Forecasting/Prediction

Im looking for the state of the art/research of time series data for forcasting/prediction. As far as im aware it is Extrem Gradient Boosting (XGBoost) or LSTM (neuronal networks) or are there other ...
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12 views

MAPE over 100% after normalization of dataset

I try to forecast power demand for next 24 hours. Years 2017 and 2018 are my training set, 2019 is test set. I use multistep vanilla LSTM . In first step I used original data with any preparation and ...
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10 views

time series forecasting of time to leave for multiple customers using one model

I am a beginner in the domain of forecasting and I was wondering if such a problem could be solved with time series analysis : given customer historical data of taxi pickups,along with the weather ...
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1answer
66 views

How fbprophet cross validation works

I am facing some issues to understand how cross_validation function works in fbprophet packages. I have a time series of 68 days (only business days) grouped by 15min and a certain metric : 00:00 ...
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1answer
23 views

Python: forecast unevenly spaced time-series?

My data has timestamps corresponding to the failure occurrences of a specific component in machinery. The timestamps are not uniformly distributed. My question is: 1) what methods can I use to (almost)...
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12 views

open source time series sales data for forecasting

I'm looking for open source time series sales data (past 2-3 yrs or more) that contains at least the following variables. ...
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2answers
20 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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1answer
39 views

Forecasting with a Machine Learning Algorithm

Im sorry if it is a too general question, but i am stuck somewhere between perfect and adequate in my model. So, i wanted to ask here. If it is not a suitable question, your negative feedbacks are all ...
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0answers
14 views

How to forecast timeseries based on different events?

I have a few IoT sensors around my house that over time store some events with timestamps. Each sensor has a unique type e.g. ‘front’ or ‘back’. Let’s call this set X. Now I have one sensor which ...
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2answers
40 views

What is an appropriate approach to sampling for probability of default using a classification model?

If we have a loan book and want to train the data to predict the probability of default, what is an appropriate way to sample the historical data to train the model, given that each account is open ...
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14 views

How to aggregate Confidence intervals?

Data Science community, I am building out a forecasting model based on Facebook's prophet algorithm. The way our business is structured, we have multiple accounts rolling up to a parent account. My ...
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18 views

Time Series Forecasting with RNN/LSTM/NARX

I have some experimental datasets (like 4 or 5), and each dataset has three time series data, say $u1(t)$, $u2(t)$, and $x(t)$. The three time series of each experiment are similar but not the same. ...
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1answer
22 views

Forecasting - 3 independent variables

I have 3 independent input variables: time, temperature and pressure, and one output - usefulness. Example: Number of the sample; Time; Temperature; Pressure; Usefulness 1;1;30;50;9% 2;3;50;50;80% ...
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25 views

How to do Goal Based Time Forecasting?

I have a yearly budget of $10,000 and I have my past monthly expenditure data. Using the past monthly expenditure data, I need to predict the date at which I'm most likely to exhaust my yearly budget. ...
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1answer
53 views

Time Series Forecasting

I work in the Oil & Gas industry. I have been trying to build a ts forecasting model with covariates, and the model R code is as follows: ...
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0answers
17 views

Vector Autoregression forecasting with large dataset

I am trying to use VAR to forecast electricity price for a whole day and I have a dataset with over 20000 observations (price for every hour) from 2015-2017. My first intuition was to select 19975 ...
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0answers
46 views

ARIMA: How to understand performance of the model?

I am new to use of ARIMA model and after working on it for a couple of days and doing research - I'm not sure how to interpret the performance of my model... Here is what the ...
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0answers
15 views

Does the R hts-Package work with time series of different units?

currently I'm having a look at the wonderful hts-Package. Its idea is to forecast hierarchical time series (like described here). In this example the unit of measurement across all time series is the ...
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36 views

Transformer Decoding in Inference mode for Time Series

With the Transformer model from "Attention is all you need" you have to feed in the the actual target during training. However, this can obviously not be done for actual inference. Now usually for ...
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16 views

How to deal with a particular month being under forecasted by the model?

I have 6 cycles of historical time series data of retail sales and using a prophet model to forecast one cycle. I am using 4 variables as regressors. There is one month in particular - 'April', which ...
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1answer
84 views

How could I improve my FB Prophet forecast?

I've got 1325 days of revenue data and when plotting the components it makes 100% sense from a domain expert point of view, so the model is capturing the variations quite well (or it seems it does...)....
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3answers
58 views

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

I have a dataset of patients where, 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 ...
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30 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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0answers
43 views

Linear correlation and XGboost regression for time series

I am working with sales time-series data, I have a history of 9 years of monthly data. I am trying to forecast sales for the next 12 months. I am using XGboost regression to build multivariate time ...
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3answers
598 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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128 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
50 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
22 views

Convey time lag information to a linear regression model

I am using a simple linear regression to predict the number of units an item has moved and price of the item is one of the input parameters. For a few items, the older prices are not relevant and ...
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1answer
592 views

Forecasting via LSTM or XGBoost… is it really a forecast or

I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts. This tutorial does a nice job explaining step by step of ...
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1answer
54 views

Forecasting time series outside the training/test set

I am trying to predict some time series based on precedent values using LSTM. I have pretty good results when I compare the predicted time series with the test set (0,18% error) I just miss how to ...
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11 views

What are good public datasets for time series analysis with “certified” (by papers in the literature) good predictors of the target variable?

I have to test different models for time series forecasting and predictors (exogenous covariates) goodness evaluation and I would like to use datasets used in relevant scientific publications that ...
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2answers
61 views

How to normalize a data set of multiple time series?

I have the a data set representing the electricity consumption of 25 000 customer. The electricity readings are taken from each smart meter each 15 min for a period of 3 days. The data is takes from ...
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1answer
26 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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63 views

How to understand logistic trend parameters proposed by Prophet (Facebook)?

I read the paper and I saw that the logistic trend is defined as below : $$ g(t) = \frac{C(t)}{1 + \exp{ -k(t) (t - m(t)) }} $$ Where $k$ and $m$ are respectively a growth rate and an offset parameter....
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39 views

LSTM forcasting: time series as input and a unique value for each as output

I am sorry for the mistakes I will make in this post, I'm new to deep learning ^^' I am trying to build an LSTM model that can help me predict some unique value according to time series indices and ...
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0answers
39 views

Problem with Prophet Model regressior

I am working on predicting half-hourly UK electricity prices with prophet. I have two other time series: gas prices and initial national demand out-turn. So, after merging all the data-sets together ...
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10 views

Context Dataset in Time Series Forecasting

In time series forecasting, what is context dataset? How different is it from the actual training dataset and what role does it play either in training or validation?
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54 views

How to combine data having similar distribution?

I have a collection of time series data with data points of around 2 years of daily data. I am thinking of a way to increase the number of data points in it so that the neural network gets a better ...
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1answer
28 views

forecasting - likelihood of customers participating in next month sales

I have historical transaction information of customers for the last 2 years and other information about the customers like what type of card (gold/platinum) they used for transactions etc. is also ...
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1answer
56 views

How to find feature importance with multiple XGBoost models

My problem statement : Time Series forecasting(Month wise data), training on 96 months of data and predicting next 12 months with a 3 months empty window in between. Example : Batch 1 ...
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31 views

Time Series and Statistical Learning methods differences

I am trying to understand the Time Series and Forecasting methods. I have this basic theoretical question about this: Why "Time Series" is NOT included on the Contents of, for example, the book "...
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13 views

Cross-validation for Timeseries Counterfactual Analysis

We are looking to predict counterfactual states from time-series data. In our problem we are looking to determine the energy savings from a grid-installed device that is varied on and off for many ...
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1answer
354 views

Forecasting multiple time series with a single model

I have a dataset with sales numbers for ~500 different markets (assume different cities or regions) and need monthly forecasts for each market. Instead of building 500 different models, I'm interested ...
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22 views

Why might an LSTM be capable of predicting an ARMA signal but not a linear combination of ARMA signals?

I have an LSTM network and am testing it on some dummy ARMA signals. I'm trying to predict the signal 5 time steps into the future. The network is capable of outperforming Naive (persistence) when ...
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13 views

For time series forecasting task, should I use data across several time steps or singe timing data for prediction?

I have a time series forecasting project, there are over 10, 000 time steps of data, so the data amount is not a problem. At first, I thought I've to create a time-based data pipeline that forms the ...
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10 views

How is possible the result of GRU would other way around compared to LSTM while they were implemented samely?

Recently I crossed to a situation I can't figure it out why it happened. I applied six predictive models on a specific dataset as training-set and tried to predict the other similar dataset as an ...
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1answer
47 views

LSTM with input of actual time step

I'm working on an implementation of LSTM neural network to forecast energy consumption. I have a dataset with load, series of weather parameters and indicator of it's bank holiday or not. I first ...
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1answer
140 views

How to make forecast in LSTM with specific/defined feature values

I am analyzing the avocado dataset to predict the future prices of avocado depending on the region and type (organic/conventional). I've trained my model which seems to be working. The test results ...
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91 views

How to time series forecast with multiple time series data sets on the same time series index

How does time series work with multiple time series data sets on the same index? For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...