Questions tagged [forecast]
The forecast tag has no usage guidance.
92
questions
0
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2answers
18 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 ...
0
votes
1answer
12 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) ...
0
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1answer
19 views
Sliding window approach using SVR & LightGBM
I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of ...
0
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0answers
13 views
Python Time Series forecast with small sample size
I was tasked with creating a Python-based time series forecast model that I could apply separately to two datasets of size n=12 and n=24. I let them know that such sample sizes would make the model ...
0
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0answers
14 views
How to determine best lagged value for feature engineering sales data forecasting
Am trying to predict daily sales of a retail product, as part of data setup have extracted sales day, dow, a year from sale date.
I would like to apply XGBOOST algorithm on the sales pre-processed ...
1
vote
1answer
31 views
ValueError from statsmodels ExponentialSmoothing
I've been having a frustrating issue with the ExponentialSmoothing module from statsmodels.
My data is a pandas series with 74 weekly data points that looks like this:
...
0
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0answers
289 views
How to fix “'The `start` argument could not be matched to a location related to the index of the data.” Error?
When I try to predict the results using ARIMA for a specific train/test split, its throwing an error like this:
"'The start argument could not be matched to a ...
0
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0answers
9 views
why forecast for test/validation set is coming as straight line for ARIMA?
I am trying to forecast values using ARIMA for 14 day data which is in a 5 minute interval [4032 values], the data was stationary hence i plotted seasonal decompose and trend was downwards, moved ...
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0answers
40 views
Error propagation in Time series forecast with many-to-many multi-steps RNN/LSTM
I am trying to do a many-to-many time series forecast, which features an encoder-decoder model to predict with variable input and fixed prediction period. In my case, I want to predict for the future ...
0
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0answers
15 views
Error decompose time series
I have a time series which represents the amount of a certain product sold throughout the year 2018. I am trying to decompose the time series but I get the following error ...
0
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1answer
77 views
Auto.arima with xreg in R, restriction on forecast periods
I am using the forecast package and implement auto.arima with xreg. Here I want to forecast ...
1
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0answers
25 views
How to interpret the graph representing the fit provided by the ARIMA model?
I'm following this tutorial here to build an ARIMA model in R.
I've done a Forecast using a fitted model in R. I specified the forecast horizon h periods ahead for predictions to be made and used ...
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0answers
11 views
How to explain calm period in energy consumption
I have a dataset of energy consumption that looks like this:
I got it from Kaggle.com and all it says is that it's energy consumption data from PJM (this is the PJMW_hourly that corresponds to the ...
3
votes
1answer
51 views
Next year forecasting with monthly data from many, correlated, non-monotonic trends
I have trend data from many health departments in a local territory (eg. cardiology, orthopedics, etc...). These trends represent health service (visits, diagnostic, admissions) production, service ...
0
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0answers
25 views
MAPE as an accuracy measure
I want to run and compare time-series forecast methods.
Mean Absolute Squared Error (MAPE) is considered one of the strongest metrics for accuracy.
My question is the following:
If you do $1-MAPE$ ...
0
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0answers
21 views
Forecast Hospitalizations for US Counties
I have a U.S. County-level data set with information on the total number of hospitalizations from 2011 to 2016. Each observation in the data set is a county and there are 8 columns in total (State ID, ...
1
vote
1answer
30 views
How can I go about building a model for large number of outputs?
I have previously worked on small-scale feedforward neural network problems.
But I have started working on a new project where the goal is to predict air quality in 25 locations throughout the ...
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0answers
11 views
What is these accuracy coefficients?
What is the basic relationship between these different measures of a model?
0
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0answers
11 views
How to forecast the next 3 months values, when you dont have supporting feature data
I have dataset of retail store, where I have values for customer contacted and purchase value. Now I want to forecast values of purchased(yellow zone) where I also want to use contacted values in ...
1
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0answers
47 views
Model for time series analysis
I'm new to data analysis and ML in general. I'm working with some friends on this problem: We're trying to predict when a component of a machine will stop working properly so the client can change it ...
1
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0answers
47 views
Predicting Wave Trends of Candelestick Charts in Tensorflow (JS)
I'm relatively new to ML but my goal it to use Tensorflow.js and build a ML model that can help me detect a certain wave formation for an automated trading system. Examples of the 3-leg pattern I am ...
3
votes
2answers
1k views
Additive vs Multiplicative model in Time Series Data
The above time series plot is a daily closing stock index of a company. I want to know which model between additive and multiplicative best suits the above data. I know what the two models are, but i ...
2
votes
2answers
203 views
Analysis of Time Series data
The below graph is a scatterplot of daily stock price. My aim is to predict future stock price of the company.
From the scatterplot it seems that it is a multiplicative model, so I tried to "...
1
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0answers
33 views
forecast product demand in one week using machine learning approach
I'm trying to predict product demand in store. The predictors I have include price, competitor's price, store ID, date. My target variable is sales volume (in a particular store). What I need to ...
2
votes
1answer
61 views
how to predict content based demand
this is my first post at ds StackExchange, so please be gentle and let me know if something is not clear :)
I have many products (>1M), and I save all the products purchases in a DB with a time stamp....
0
votes
1answer
22 views
ML technique to predict next year output based on text quantities [closed]
I have a random data that I would like to predict how much a quantity will be in 2020.
The data looks like this:
...
0
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0answers
29 views
Multivariate time series forecasting without historical data
I have a dataset similar to the example below:
It contains some categorical data (e.g. business type, business size, location) and the time-series of energy consumption from Time 1 to Time N with a ...
1
vote
0answers
36 views
Tuning a sequence to sequence model
I have written a variable length sequence to seqeunce autoencoder in keras using this tutorial as a guideline: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras....
3
votes
1answer
76 views
Is an Arma model equivalent to a 1-layer Recurrent Neural Network without activation function?
Given a time series $f(t)$ to forecast, let us consider an Arma model of the form:
$$
f(t) = c + \sum_{i=1}^p a_i f(t-i) + e(t) + \sum_{j=1}^q b_j e(t-j)
$$
where $e(t)$ are the forecast errors.
On ...
1
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0answers
1k views
Monthly trend with fb prophet
I have monthly data with month/year in one column and price on another. I would like to get a yearly trend with fb prophet library in python (how to use monthly data with the library is explained at ...
1
vote
0answers
21 views
Specific data formatting techniques for discontiguous time series?
I'm facing a predicting problem for food alerts. The goal is to predict the variables of the most probable alert in the next x days (also any information I could get about future alerts is really ...
1
vote
0answers
17 views
Observation Operator - Data Assimilation
In data assimilation, one assumes the existence of a observation operator $\mathcal{H}$ that maps the model-state vector $\bf{x_b}$ to $
\bf{y_b}$ (the model-equivalent of the observations $\bf{y_o}$) ...
1
vote
1answer
27 views
Strategies for continuously assessing and improving model performance
I am building a supervised machine learning model to generate forecast.
So I would have historic data like this:
SKU, Month, .... other features, Actual Volume
...
0
votes
1answer
875 views
How to calculate prediction error in a LSTM keras
I have an LSTM which I have constructed and run in keras using python. I use this model to predict $n$ points into the future for a time series forecasting problem.
When I use a method such as ...
1
vote
2answers
50 views
What are some appropriate models to use for inventory forecast based on consumption history or trends?
I am working on an inventory management system where I have daily/monthly/yearly consumption history for a particular item, which may or may not follow a repeating trend. In order to forecast demands ...
0
votes
1answer
27 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
...
1
vote
1answer
59 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 ...
2
votes
1answer
375 views
demand forecast for B2B
I am attempting to create a demand forecasting model in python to predict future sales of a particular category of product, using historical sales data.
We are a B2B company, which means that we ...
1
vote
0answers
62 views
Naive/ Persistent Models & 7 Day Forecasts
When determining the baseline performance using a persistent or naive model I understand it to be using the value of the previous time step as the prediction for the next time step and then ...
1
vote
0answers
87 views
R codes for “Matrix estimation by Universal Singular Value Thresholding”
Consider a real demand estimation problem of a retailer where matrix Y is the real demand need to be estimated by using sales data (matrix) X which is bounded by stock (matrix) C.
The estimation ...
0
votes
1answer
163 views
A timestep's prediction depends on future data
Consider an LSTM model with 100 timesteps, each of which with input and target data. Let f(99) be the function mapping the input data of the 99th timestep and hidden state of the 98th timestep to the ...
0
votes
1answer
270 views
Modelling promotions for demand forecasting
I am trying to develop a model to predict future demand for a product. Now, there are always some promotional events that affect the sales. I am trying to solve this problem using dummy variables. ...
0
votes
2answers
574 views
Fitting an arimax model on out of sample dataset
I have built an arimax model where we have sales data across time as the response variable and price is one of the external variables. I used the below code to build a simple arimax model. I had data ...
4
votes
1answer
1k views
Time series forecasting using multiple time series as training data
I am trying to forecast the total attendance (ie. the number of entrances, which is also the number of tickets bought) of a festival just two days after it started. That is, knowing how many people ...
0
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3answers
187 views
Forecast vs Prediction: What is the difference?
I use the two terms as follows:
A prediction model gets features (which can be a time series) as input and gives a fixed-length output (might be multiple values, but "atomic" in some sense)
Examples:...
2
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0answers
3k views
3
votes
1answer
555 views
how to decide categorical variables for prediction
I have a dataset that contains weekly sales for stores and categories. It looks like this:
I would like to apply gradient boosting method to predict weekly sales. My question is: Should I create ...
1
vote
1answer
3k views
Demand Forecasting for Multiple Products Across Thousands of Stores
I'm currently working on a demand forecasting task, with data on tens of thousands of products across a couple thousand stores. More specifically,I have 3 years' worth of daily sales data per product ...
3
votes
2answers
656 views
How do I perform multi-step forecasting on LSTM trained with multiple observations of a sequence?
I have a trained sequence-to-sequence LSTM that predicts a person's velocity trace (Δx, Δy) given their position (x, y) as they walk along a path. I trained the LSTM using 200 examples of different ...
0
votes
1answer
80 views
Choice of time series models
I built a model for time series in order to forecast new values.
What is the best way to choose the correct model?
Are better criteria like AIC and BIC or the comparison between prediction errors? ...
