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Questions tagged [arima]

arima (autoregressive integrated moving average.) It's a model used in data science to measure events that happen over a period of time. The model is used to understand past data or predict future data in time series.

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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 ...
Myron's user avatar
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4 votes
4 answers
1k views

Python (S)ARIMA models completely wrong

I have some time series, like this one: I want to predict future values, so I splitted in train/test (70/30) and I created several ARIMA models, however they are all completely wrong (or maybe I am ...
CasellaJr's user avatar
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4 votes
1 answer
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How to know if a time series sequence is predictibale or just random (Univariate time series prediction)?

I'm trying to predict a current value of a variable based on the its previous 10 values. I tried multiple time series approaches including ARIMA, LSTM and linear regression... None of them really ...
the phoenix's user avatar
4 votes
2 answers
8k 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 ...
krinker's user avatar
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3 votes
3 answers
4k views

Best common metric for comparing classic time series forecasting methods (ARIMA/Prophet) with ML approach?

I am new to time series forecasting and looking to compare the performance of ARIMA/Prophet with an XGBoost model in predicting future stock market values based on historical stock market data and ...
Darcey BM's user avatar
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3 votes
1 answer
117 views

How to use ARIMA to predict time series?

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Amogh Katwe's user avatar
3 votes
1 answer
9k views

XGBoost vs ARIMA for Time Series analysis

Doing time series analysis, I have doubts on choosing the right model. I want to predict the next 30 mins window, from the input dataset which contains the no. of error count for that particular 1 min ...
Jai K's user avatar
  • 151
3 votes
1 answer
146 views

ML model to forecast time series data

This question has three sub-parts, answering each of which probably doesn't require huge text. I hope that is okay. I'm trying to understand time series prediction using ML. I have the target variable ...
user9343456's user avatar
2 votes
1 answer
52 views

Beginner Question on ARIMA

I have started learning time series forecasting and struggling a bit with the concept of differencing, particularly for (S)ARIMA(X) model, which is often recommended model to start with. I am trying ...
miroslaavi's user avatar
2 votes
2 answers
251 views

High error Arima model - Python

I have a time series data. It has daily frequency. I want to forecast the data for the next week or month with an ARIMA model. This is a chart of my time series data: First I use the method ...
J.C Guzman's user avatar
2 votes
1 answer
294 views

ARIMA predictions look shifted by one unit of time

I am using statsmodels ARIMA (1,2,1) to predict the monthly demand for a product. The predictions look like they are shifted to the right by one month. I wonder if the statsmodels.ARIMA.Residuals....
Gloksinya's user avatar
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2 votes
0 answers
1k views

Grouped Time Series forecasting with scikit-hts

I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in ...
Downforu's user avatar
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2 votes
0 answers
23 views

How to automate Seasonal Arima?

I am building Seasonal Arima for more than 10k products. In all the tutorials and blogs mentioned, I need to do the exploration to find the p,d,q values along with seasonality value using the ...
Jack Daniel's user avatar
2 votes
0 answers
276 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 ...
DGomonov's user avatar
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2 votes
0 answers
569 views

Multiple seasonality with ARIMA?

I know that ARIMA can't detect multiple seasonality, but it is possible to use fourier functions to add a second seasonality. I need to forecast gas consumption composed by a daily, weekly (week days-...
marcodena's user avatar
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1 vote
1 answer
327 views

Why so discrepancy between ARIMA and LSTM in time series forecasting?

I have this time series below, that I divided into train, val and test: Basically, I trained an ARIMA and an LSTM on those data, and results are completely different, in terms of prediction: ARIMA: ...
CasellaJr's user avatar
  • 229
1 vote
1 answer
98 views

How do I work with time-series data of temperature?

So I have some equipment temperature and i have outside temperature (both are collected daily) and I want to predict the equipment temperature. However, I'm new to this and unsure about which model to ...
Ria's user avatar
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1 vote
1 answer
77 views

Predict the first observations of a time series when order of the model is higher

Suppose you have you have a time series with 365 observations, one for each day of the year, and you split the first 183 rows in training set and the latest 182 in test set. Suppose you create an AR (...
CasellaJr's user avatar
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1 vote
1 answer
113 views

Why is my prediction using ARIMA better if I'm using less historic data?

I have a data set containing hourly electricity prices for since 1.01.19 until September. Since the process turned out to be (weakly) stationary, I applied an ARIMA model in Python in order to predict ...
Fabulini's user avatar
1 vote
1 answer
3k views

Categorical variable for Arima

Does Arima support the usage of categorical variable? Some ways to get it working can be using one-hot encoding to represent categorical variables, but I am not sure how good it is.
william007's user avatar
1 vote
1 answer
518 views

Why MA model order is from acf but not pacf

For MA model in Arima, why the order references acf, but not pacf? The emphasize is why not PACF. In https://towardsdatascience.com/significance-of-acf-and-pacf-plots-in-time-series-analysis-...
william007's user avatar
1 vote
1 answer
169 views

Seasonal ARIMA?

Greatly enjoy exploring data in Orange Data Mining! I have daily average temperature data for several years. I can plot the periodogram, and do a seasonal decompose. Is there a way to forecast the ...
Gamer 007's user avatar
1 vote
0 answers
46 views

Does this ARIMA model take seasonality into account?

I'm writing a tutorial on traditional time series forecasting models. One key issue with ARIMA models is that they cannot model seasonal data. So, I wanted to get some seasonal data and show that the ...
codeananda's user avatar
1 vote
0 answers
89 views

How to compare different forecasting models over different time horizon?

Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA. Now, the purpose is to find out which model performs best on a given use case with different time horizons. The time series data is ...
Mannheimer_Coder's user avatar
1 vote
0 answers
45 views

How can i find the accurcy for time series? [closed]

I have to work for the first time with time series and I have some question about this interesting field of machine learning. What I have to do: I should make a forecast for quantity for some special ...
JiJoik's user avatar
  • 47
1 vote
0 answers
395 views

auto_arima results summary (intercept?)

I ran auto_arima on my model from the library pmdarima and I'm trying to interpret the results printed to the console. I see two examples of similar parameters that yield different results: ARIMA(0,1,...
Oliver Foster's user avatar
1 vote
1 answer
185 views

Time series forecast for everyday for till a distant future

I have time-series data for every single day from the last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every ...
Hamza's user avatar
  • 143
1 vote
0 answers
15 views

Ljung-box test on weekly percentage of total quarter bookings

I have a data on the weekly percentage of the total quarter bookings. The data looks as follows (note: weekly percentages add up to 100 for each quarter) : (not real data) I used the Ljung-box test ...
Saqib Ali's user avatar
  • 111
1 vote
0 answers
118 views

Understanding lag plot ,ACF plot and auto-correlation plots

I have a data-frame of 2809 rows which is an unevenly spaced time-series ...
Devarshi Goswami's user avatar
1 vote
1 answer
109 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) ...
Raffaele Giannella's user avatar
1 vote
1 answer
3k views

Python: SARIMAX Model Fits too slow

I have a time series data with the date and temperature records of a city. Following are my observations from the time series ...
Subhawna's user avatar
0 votes
1 answer
52 views

Should I choose an ARIMA model (2,1,1) with a higher AIC value or an ARIMA model (6,1,8) with a lower AIC value?

I am trying to fit an ARIMA model to time series data. When I fit the model using auto.arima function in R, ...
Mehmet Yildirim's user avatar
0 votes
1 answer
62 views

Timeseries Sales Forecasting

In my current work sales forecasting and budgeting is being done rather classical way: Take the sales from last year for comparable date and add or decrease X% on top to reflect recent trend. This ...
miroslaavi's user avatar
0 votes
2 answers
329 views

Supervised or Unsupervised Learning Classification: Facebook Prophet vs. ARIMA

I'm currently exploring time series forecasting and considering the use of Facebook's Prophet and ARIMA models. I'm a bit confused about whether these approaches fall under supervised or unsupervised ...
Linear Data Structure's user avatar
0 votes
2 answers
101 views

How can we make forecasts from stationary data

I'm confused about the concept of stationarity. Most definitions require the mean and Variance to be constant 'over any interval'. This statement confuses me, if any interval should have the same mean ...
Aditya Prakash's user avatar
0 votes
2 answers
4k views

Time series forecast for small data set

I am new in data science so please accept my apology in advance if my question sounds stupid. I want to do a time series forecast of outage mins in the current regulatory year. The regulatory year ...
user2293224's user avatar
0 votes
1 answer
169 views

Time Series: ARIMA vs Random Forest Regressor

I have two model prediction results: Using ARIMA model Using Machine learning model where I used Random Forest Regressor How do we compare these two? Is conventional time series modelling better or,...
Athos's user avatar
  • 1
0 votes
1 answer
482 views

Remove Seasonality before applying SARIMA model on weekly data?

I am trying to predict average weekly stock prices for time series data. Steps I followed: I tested the data to check whether it was stationary or not using ADF ...
Kriti's user avatar
  • 363
0 votes
1 answer
308 views

Which dataset for multivariate time series forecasting

I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ? Can I use a dataset ...
Djakarta_zero's user avatar
0 votes
1 answer
605 views

Applicability of ARIMA model on non stationary data

I have a time series dataset that does not have the stationary property. The dataset is monotonically increasing or sometimes showing no change over periods of time. Can I apply the ARIMA model to ...
Jd_mahmud's user avatar
0 votes
1 answer
294 views

How to handle multi time series data for 10K + items

There are 50 shops and each shop have 30000 items. Goal is to forecast the sale of item based on shop. Forecase the item_cnt_day, for this i dont see this as multi ...
Shan Khan's user avatar
  • 133
0 votes
1 answer
50 views

What are more advanced techniques than ARIMA?

For timeseries predication cases, what other techniques are available in statistics or machine/deep learning other than MA (moving average), ARMA, and ARIMA?
Sandeep Bhutani's user avatar
0 votes
1 answer
1k views

First Differencing to remove seasonality and trends

I am trying to remove seasonality and trends from my time series data. I found this post that said to use df_diff = df.diff().diff(12).dropna() (https://www.tobiolabode.com/blog/2020/12/30/how-to-...
user118151's user avatar
0 votes
0 answers
25 views

ARIMA Model shows flatline on predictions

I am learning time series for first time and trying it out on some kaggle datasets. For daily stock price predictions/weather prediction (https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-...
AJA's user avatar
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0 votes
0 answers
14 views

Is it possible to use leftovers data (warehouse stocks data) to create sales forecasting?

For example, i have sales data by categories | Date | GE | VIC | | -- | -- | --| |03.01.2022 |2|7| |10.01.2022 |30 |12| |17.01.2022 |15 |5| |24.01.2022 |57 |8| |.....|...|...| |28.08.2023 |16 |2| And ...
Holo's user avatar
  • 1
0 votes
0 answers
28 views

Model for small sample time series

Im a total novice and i need to estimate some kind of relation-proof model(Granger test results, correl matrix are already provide some evidence) with following dataset: 20 observations (2001-2021), 4 ...
Maqar Mocha's user avatar
0 votes
0 answers
21 views

Predicting quanting sold using Time series data

I am struggling with a time series dataset comprising 12 features, including quantity sold and weather data, totaling approximately 1800 values. My goal has been to forecast future values, quantity ...
BasicTex's user avatar
0 votes
0 answers
14 views

Is Kernel Density Estimation actually the continuous/curved version of a histogram?

I have been studying ARIMA for a bit of time now, and stumbled upon the plot_diagnostics function. Among others, it plots out the Histogram & KDE on the same plot. As I did not know anything about ...
brewandrew's user avatar
0 votes
1 answer
34 views

How to predict in a forecasting model when the data after training and prediction is missing?

Let's say we have a forecasting model that was trained on any data before 2021 and now we need to make a prediction on data in 2023, for an accurate prediction we need to either give the data of 2022 ...
Sadaf Shafi's user avatar
0 votes
0 answers
11 views

Interpretting ar coefficient in arma proccess

I'm currently studying autocorrelation in Python and exploring the ArmaProcess module within the statsmodels.tsa.arima_process ...
Ali Sadeghi Aghili's user avatar